Integrating Semantic Web Technologies in Higher Education: A Decision Support System for University Selection

Integrating Semantic Web Technologies in Higher Education: A Decision Support System for University Selection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrating Semantic Web Technologies in Higher Education: A Decision Support System for University Selection Shabir Ali Shah, Malik Sikandar Hayat Khiyal, Mohammad Daud Awan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4135589/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract The process of choosing an appropriate university is a significant and complex decision for both students and academics. The challenge lies in efficiently navigating and interpreting the vast amount of unstructured and semi-structured data available online. To tackle this issue, this paper introduces a sophisticated decision support system designed to aggregate and integrate data from diverse sources, such as university websites and other online platforms. This system employs semantic web technologies and various datasets for automatic information extraction, facilitating a more streamlined data analysis process. Key attributes such as research output, student demographics, geographical location, and overall rankings are utilized as parameters for data integration. The system is equipped with a user-friendly interface that offers customizable visualization tools. These tools enable users to prioritize and compare universities based on their individual preferences effectively. For empirical validation, we gathered and analyzed data from 301 universities in the United Kingdom, employing both manual and automated techniques for information extraction. The outcomes of this study underscore the efficiency and practicality of our approach in simplifying the university selection process for potential students and faculty members. Overall, this system presents itself as an invaluable resource for informed decision-making in the domain of higher education. Information aggregation Information visualization Semantic web Information retrieval Decision Support System Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction The rapid advancement of computer visualization techniques, alongside the growth of virtual and augmented reality, has significantly enhanced the possibilities for data visualization and the creation of immersive virtual spaces for educational purposes. This technological progress offers promising avenues for the effective presentation of information and the creation of engaging environments that cater to the needs of the new generation. In today's digital age, the journey towards higher education is navigated through a vast sea of information, making the selection of a suitable university a daunting task for students, educators, and researchers alike. With over 31,097 universities worldwide, the task of selecting a university becomes even more daunting. This highlights the need for a decision support system specifically tailored for university selection, capable of integrating data from various sources into a single, user-friendly platform. Such a system can offer a holistic view of universities, encompassing academic programs, faculty, campus facilities, student satisfaction, and financial aid options, thereby streamlining the university selection process and saving valuable time for students. Based on the aforementioned notion, Fig. 1 depicts key steps. Data Layer: This is the foundational layer where the data is collected from the RDF knowledge bases and websites. It includes metrics such as the number of papers published, faculty count, details about foreign students, the ratio of students supervised by teachers, and information on various academic programs (BS, Masters, PhD). Aggregator: This step combines and processes data from the data layer, making it more structured and meaningful for analysis. Mining Layer: At this level, advanced data processing techniques are applied to the aggregated data to extract valuable insights, patterns, and trends. Users: The final layer where the processed data is made available to various end-users, such as students (for making informed decisions about admissions), teachers (for identifying collaboration opportunities and funding sources), universities, and libraries. submission. It will speed up the review and typesetting process. At the heart of our system lies the integration of advanced visualization tools and semantic web technologies. These components work synergistically to curate and present data from a wide array of online resources, including academic publications, university rankings, and student reviews. By transforming raw data into intuitive graphical representations, the system allows users to easily navigate through complex information landscapes, enabling a deeper understanding of each university's offerings, ethos, and performance metrics [ 1 , 2 ]. This paper introduces a groundbreaking decision support system that harnesses the power of visualization and semantic web technologies to streamline the university selection process. By aggregating data from various online sources, this system offers a comprehensive platform that not only simplifies data analysis but also enhances decision-making through intuitive, user-centric visualization tools. These tools enable stakeholders to compare universities across multiple parameters, including academic programs, research output, campus facilities, and overall rankings, tailored to their unique preferences and needs. With an emphasis on the diverse perspectives of its users, the system facilitates a more informed, efficient, and personalized approach to university selection. This innovative approach promises to transform the complex landscape of higher education decision-making, providing a valuable resource for prospective students, faculty members seeking collaborations, and parents alike, in their quest to find the best academic fit. The proposed system provides an intuitive visualization that enables users to rank universities based on their preferences and compare them comprehensively. The system was tested on data from 301 UK universities, and results showed that the proposed approach is effective in facilitating the university selection process for students and professors. The system can serve as a valuable tool for decision-making and support in the higher education sector. 2 Related Work The selection of a suitable university is an important decision for different people. Their decision is based on a number of factors such as research environment, student strength, geographical location, course selection, and the overall ranking of the university. These requirements are normally followed by surfacing across the search engines/web portals, with different query strings and having hundreds of thousands of pages which further require manual inspection. Recommender systems: Today, there are numerous recommender systems accessible in many different fields that make it easier for people to go about their daily lives. Information visualization techniques: Information visualization is a strategy for presenting data in a meaningful and illustrative manner that people can readily analyze and comprehend. 2.1 Online Systems/Web Portals A number of systems are available online that are being used by the community to find suitable universities/colleges. For example, the School Finder system is being used for Canadian colleges and schools since 1995. With the help of the School Finder system users can find out about the graduate/Professional and undergraduate programs which are offered by the Canadian school and colleges. When anyone wants to find or search about the program then he will write the desired program in the search box and it will show a user/person of all the Schools/Colleges where the Programs/Education subjects are taught at all levels in the graduate, undergraduate, and secondary level. But there is no concept of visualization and comparison of these programs with each other and which one is the best school or college among all rank-wise. College Finder provides information about thousands of schools/colleges all over Canada, in this system a person/user can use different parameters, and a person/user can also search about the school/college based upon his/her religious concept in which he/she is more interested. College Finder provides a search through their extensive database and finds colleges/universities with a person’s/user’s religious beliefs also. There are different parameters used for search criteria. The following criteria are used for searching 1) near to his location, 2) academic standards, 3) cost, 4) party scene, and 5) Greek life and majors offered. But there is no concept of visualization and it does not show us any comparison of different universities/colleges. In the online University Finder system user can use the information to find their choice of university or college online in the United States. This system provides complete information about all the universities and colleges across the country which are existing in the United States. One can access this information at his/her fingertips if he/she is having a reliable internet service and also a system, financial aid, an academic background, and more athletics. This system also provides the comprehensive profiles of all universities in the United States. A person can receive information on obtaining a university degree online and one can sit at home, apply online to one of the best universities or colleges in the United States, study online, and then obtain an online degree. Online university finder systems can be used to find the users desired university or college online in the United States. The system provides complete information about all the universities and colleges across the country which are located in the United States. Online university finder system also gives student loans for study and one can search for the best university and cheap university for study. But there is no concept of visualization and compression of these universities with each other and which one is the best among these universities located in the United States. Unistats is the official website of the UK. This system helps the user when anyone wants to apply to UK universities/colleges. On this website there are two types of search options for users 1) in which subject the user is interested to study? 2) In which university or college, the user is interested to apply? But again, it’s very difficult to see that which subject is taught in which university and then compare the universities with each other’s. 2.2 Visualization Techniques The authors [ 12 ] explain that WWW is currently experiencing revolutionary growth due to various emerging tools, techniques, and concepts. In this paper [ 13 ] the author also discovers and gives the solution to the problems while using an emerging Web 2.0 technology and he explores the application of mashups for the Journal of Universal Computer Science (JUCS) and encourages the readers, and authors to use that application. There is continuous improvement in Electronic journals and their services based on technological developments. Great effort has been made in providing high-level access options to the users of e-collections using 2D or 3D maps involving the semantic analysis and visualization of relevant topics [ 14 ]. Some important aspects of modern digital libraries like searching intelligently, to visualizing the search results, have been discussed in [ 15 ]. The author expands the publication of J.UCS and it is necessary to know about the current state of readership and accessibility of journals the author also determines which locations (cities and institutes) are contributing less, more, and which have stopped contributing [ 16 ]. Many universities developed web-based campus maps [ 20 ]. The spatial features of the geographical information system (GIS) are added in such types of maps for facility visual searches. The author discussed that the purpose of this paper is to design and build up an interactive, easy, and web-based Beytepe Campus Map to process visual queries on the geographical information system (GIS) and make it available through the Hacettepe University website, the main categories have also sub-categories to present the places at Beytepe Campus 1) Academic units,2) Administrative unit, 3) Sheltering, 4) Nutrition, 5) Health, 6) Transportation, 7) Entertainment, and 8) sports center. The author discussed Vimo in the integrated comparison portal released in January 2006. Vimo allows US rates, and purchases health insurance and health saving accounts, and also selects the physician profiles from across the US. Vimo can find physicians and compare prices of each hospital and also there is a facility to allow the users to read and post reviews. The author uses Google Map API which searches the exact location of a health professional’s office when the consumer clicks on the name of Google Map, Health map brings different data sources together to complete a combined and comprehensive view of the present state of infectious diseases and also the effect of these diseases on human and the effect on animal health. Google Maps also provide the latitude and longitude to show the exact hospital location and address but the author has not mentioned here about which hospital is better than the other hospitals. The author [ 16 ] explains that JUCS is a unique electronic journal of computer science having more than Fifteen hundred (1500) research publication in different domains of computer science and added so many new ideas and features recently, which includes semantic searching as well as annotative option and collaborative option. JUCS is 1st electronic journal that implemented personal annotations and public annotations. JUCS also implemented research publication in multi-format options and define the category in multiples way etc. The author [ 17 ] also explored the distribution of authors who published papers in JUCS and editors across the world. Moreover, JUCS keeps up all the data for authors and editors which includes their country information, city information, and university information. The author 1st developed two (02) types of options. In 1st option visualization of author information with JUCS data on Google Map API. 2nd visualization of author distribution geographically and the Zooming option is also available. For full visualization, the author used manual effort and somewhere updating blank files for all this to be more effective automated technique is very much necessary. While using automated techniques one can better explore the city name and country name of the university in the search query Google Map API with author information. There is no comparison among authors, that shows users about the author’s publication that in which area he is so strong and how many publications he did in that category etc. Cartography [ 18 ] is the word used as the art and science of making maps and historically used by Geography as its language from this cartography gained new tools and media that enhanced the static maps and introduced multiple layers. The term Geographic Visualization or “GeoVisualization” (Gvis) refers to spatial data and can be used for all layers of problem-solving in geographical analysis. In the paper, the author presented two applications of Google Maps API in which health and higher education data overlaid as a thematic layer on top of the standard Google Maps base layer. The Data used to generate health applications result from registered patients within 48 General Practices in Southwark Primary Care Trust, situated in the region of Southwark, South London. These data are together through a system called Exeter and are used to monitor the practice activity against the Quality and Outcome Framework (QOF). The data used within the Higher Education (HE) mashup use an extract of the Universities and Colleges Admissions Service (UCAS) applicant database which was created during the 2004 application cycle. The author also discussed the advantages of data visualization and how to use different colors, fonts, and layouts while using data visualization techniques and with the help of data visualization, users avoid potential pitfalls [ 13 ]. The author explained that the Convenience store area is dependably the important feature, which is additionally a vital factor. Appropriate stores cannot just decide the number of store customers, numerous elements decide the accomplishment of a convenience store, and the nature of the store address assumes a key part in the achievement of the accommodation store. The author presented data visualization innovation and data recovery innovation in light of the investigation of the innovation of data visualization and geographic spatial metadata [ 14 ]. The creator additionally said that shading and geometry portrayals are quickly perceived by the human’s cerebrum, and information representation advances furnished by information mining come about with characteristic and natural activity interfaces. The fulfillment of the client’s request for such huge numbers of information mining emphasis was performed. The examination is as yet progressing and he is constantly upgrading and creating basic information mining calculation and information representation models in the framework [ 10 ]. The author discussed that the visualization technique is not a new concept but by using data visualization one can present the data graphically or pictorially and also discussed the free sources of data and the requirement of user-created content on social media have also led to rise in popularity of data visualization concept. The author also discussed the advantages of data visualization and how to use different colors, fonts, and layouts while using data visualization techniques and with the help of data visualization, users avoid potential pitfalls. Librarians and other information professionals are using data visualization to generate annual reports and insightful internal library appraisals. Library staff can also prepare themselves to teach and assist others in creating captivating data-driven visualization [ 19 ]. 2.3 Decision Support Systems The expansion of schools and the increasing number of campuses, departments, teachers, and students call for the development of an open and efficient online school administration system. The current resource management platform poses challenges for sharing educational resources due to its varied structures. This study [ 21 ] proposes an information integration platform based on Service-Oriented Architecture (SOA) to unify various enterprise application systems, enabling information sharing and meeting cross-departmental business needs. The proposed platform minimizes the impact of demand changes, enhances flexibility, and streamlines educational administration processes. By adopting SOA and Web services, the platform integrates existing information system resources, saves development costs, and improves performance management quality. The loosely coupled and reusable module design allows for seamless integration and reduces development complexity. The use of Web services technology simplifies system deployment and usage. It is expected that as the support for SOA advances, this SO-based information integration platform will find broader applications in enterprise informatization. In this paper, the author [ 22 ] explained that technological advances necessitate collaborative efforts among universities, teachers, and students to restructure departments and courses. Failing to do so risks reduced quality and competitiveness. To address this, a decision support system is proposed with three stages: data collection, conversion into meaningful information using natural language processing, and ranking alternatives using multi-criteria decision-making. This system benefits universities by informing department and course offerings, helping teachers create or shape courses, and guiding students in their choices. Experimental validation using computer engineering job postings and course contents from Turkish universities confirm the system's applicability and reliability. This study [ 23 ] focuses on developing an online learning support system using location-based service architecture. The research analyzes learning result data and implements an improved algorithm to enhance accuracy. The study concludes that the learning support service system plays a role in improving the quality of online education and advancing its development. By combining location-based service architecture with the learning system, the study introduces a new research direction. The algorithm improves content-based recommendations by incorporating weighted recommendation results based on geographic information, location preferences, and user decision-making. The system tracks students' real-time progress, provides personalized guidance, and effectively monitors learning progress and proficiency. The evaluation system considers students' achievements and abilities, offering scientific and personalized service. The learning support system employs big data, learning analysis, and mobile Internet technology to provide intelligent and humanized support services, while the application of location-based service architecture introduces a new evaluation and teaching method, quantifying learning states and reflecting learning effects comprehensively and objectively. The article [ 24 ] addresses the concern of low graduation rates at four-year state colleges, despite the use of academic indicators in the admission process. The authors suggest using an ensemble of analytic models that incorporate cost analysis to inform decision support systems. By analyzing ten years of data for 10,000 students and applying ten different models, the research aims to identify the best predictor of at-risk students. The study also utilizes the receiver operating characteristic curve to determine the optimal balance between false positive and false negative levels to achieve cost-effectiveness. This paper [ 25 ] focuses on analyzing students' physical education information, course exam results, and learning data from an online teaching platform using the forest algorithm and decision tree algorithm. The objective is to generate decision trees and classification rules to identify factors influencing students' physical education performance. By constructing a model for assessing teaching effectiveness, the study aims to improve teaching quality and strategies. The research includes data collection, preprocessing, model construction, algorithm optimization, and simulation results. The CART algorithm is specifically applied to analyze student data and predict their effectiveness in physical education. The study highlights the importance of effective teaching methods in e-learning platforms and suggests pedagogical adjustments based on the identified rules. Decision trees and random forests are chosen due to their clarity, simplicity, computational efficiency, and accuracy. The application of CART algorithms in assessing student effectiveness in physical education holds significance. This paper discusses [ 26 ] the separation of decision modeling from process modeling and introduces a Decision as a Service (DaaS) layered Service-Oriented Architecture (SOA). The DaaS approach treats decisions as automated and externalized services that processes can invoke on demand. The paper formalizes the DaaS framework using Decision Model and Notation (DMN) constructs and evaluates its adherence to SOA principles such as abstraction, reusability, and loose coupling. The benefits of the DaaS design on process-decision modeling and mining are discussed, and a real-life example of a bank loan application and approval process is used to illustrate the DaaS design. The paper contributes to the understanding of the interaction between decisions and processes and demonstrates the scalability, maintainability, flexibility, and understandability provided by the DaaS design. The proposed framework enhances integrated process-decision modeling and shows promising results in real-life event logs. The importance of successful internships for students' future careers is recognized, and a decision support model is proposed to enhance the assignment process in higher education. The model consists of seven phases, which can be extended to nine phases, including students' choice of internship place. The model is iterative and interactive, involving the course coordinator and students. Results from four scenarios validate the model, showing a high correlation between students and internship proposals. The proposed decision support system aims to complement the manual assignment process, which becomes challenging with a large number of students and proposals. The model incorporates objective and subjective evaluation elements to improve student and company satisfaction. However, limitations include the inability to measure the impact on student employability and the difficulty of quantifying soft skills. The model requires further testing in different scenarios and institutions [ 27 ]. This study examines [ 28 ] the role of estimated risk in educational choices and its impact on educational inequalities, specifically focusing on social background differences. Using data from the ISCY Project in Barcelona, the study analyzes the estimated risk in higher education access. The findings reveal disparities in estimated risk based on social and economic factors. By operationalizing and contrasting the concept of estimated risk, the study demonstrates its usefulness as a framework for explaining educational inequalities and evaluating educational policies. Students' educational choices are influenced by their social background, leading to educational segmentation and the potential reproduction of social inequalities. The study explores the role of risk management in educational choices, considering factors such as motivations, academic abilities, and resources. Survey data are used to operationalize economic, academic, and social risks estimated by students and examine their relationship with actual choices. Despite the ongoing concern about graduation rates at four-year state colleges, little improvement has been made in overall graduation rates. Academic indicators like high school GPA and ACT/SAT scores have long been used for selective admission, yet recent statistics indicate that less than 40% of students graduate within four years in the US. To address this issue, the authors propose an ensemble of analytic models that consider cost as a more effective approach for decision support systems. The study [ 29 ] analyzes ten years of data for 10,000 students and applies ten analytical models to identify at-risk students. By using the receiver operating characteristic curve, the research determines the optimal balance between false positive and false negative levels. Implementing a decision support system with predictive analytics can help identify at-risk students early on and implement interventions to prevent dropout. This approach enables administrators to make cost-effective decisions and utilize limited resources efficiently. By focusing on first and second-semester dropouts, timely decision-making and assessment of the effectiveness of administrative changes can be achieved. The article concludes with discussions and recommendations on modeling and practical applications within resource constraints. The paper [ 30 ] proposes an approach to building configurable service-oriented decision support systems through automated service composition, which simplifies the development process. The results presented include a functional framework for different types of decision support systems, requirements for configurable service-oriented systems and their components, and a conceptual model for such systems. This novel approach enables the development of problem-specific decision support systems that can be used with little or no special training, accelerating the development cycle. Future work involves encoding typical service compositions and creating a methodology for generating services as building blocks in these systems. A decision support system was developed to assist the community in selecting a suitable college based on their capabilities and job demands. The system uses the Simple Additive Weighting (SAW) method to provide recommendations to users by considering predetermined criteria. The study [ 31 ] concludes that SAW is effective in solving the selection of universities problem, with accreditation being the most prioritized criterion. Suggestions for further improvement include exploring other decision-making methods, incorporating additional criteria, and utilizing a computer application to streamline the decision-making process. Automating and optimizing the creation of timetables for educational institutions is crucial to reduce costs. Previous studies on this problem have been based on unrealistic models with limited practical application. This paper [ 32 ] summarizes the work by Bullet Solutions, which focused on understanding and modeling the problem, developing robust algorithms, and employing optimization methods. The BTTE application, resulting from this work, achieved high-quality results with significant time savings (85%) in all analyzed cases. The application improved processes, centralizing and organizing information, increasing workflow efficiency, and aligning institutions with digital society procedures. The use of advanced technology for automation and optimization has enhanced the image and positioning of institutions while providing top management with greater control over teaching services. Notably, considerable savings in teacher hiring have been realized through the implementation of the BTTE application. A study [ 33 ] was conducted to develop a Data-Driven Education Decision Support System (DDEDSS) as an innovative tool for educational decision-making. The DDEDSS software prototype was designed and tested using education data from two sessions. The system successfully evaluated learners' performance and provided a basis for curriculum optimization and class adjustments. The research demonstrated the significance of DDEDSS in educational research. The study focused on data acquisition, storage, integration, analysis, and mining, using SQL Server 2008 as the tool software. However, the development of the DDEDSS software faced challenges in utilizing built-in data analysis and mining functions. The study also identified the alignment between the five levels of data processing and the subject system's levels of practice, technology, science, sentiment, and philosophy, further validating the feasibility of the information and interaction system. 3 Proposed Technique A Decision Support System for University Selection is shown in Fig. 2 . The overall architecture of the system is divided into four main layers such as 1) Data Collector, 2) University Data Files, 3) Mash-up, and 4) Visualization components. The Data Collector component consists of sub-components that load the RDF triplets from the RDF store, and convert them to MySQL database format for SQL queries in the future. The RDF store has various properties like the subject, author, etc. The RDF Parser converts it and stores it in the MySQL database. This conversion enables us to build a user-friendly interface containing a query posting mechanism and searches for a total number of papers published in the universities. This information is extracted from UK universities’ RDF, next Data stored in files, next using pre-processing the data and finally organized the data and stored in the MySQL database and then using Google map API and use the dojo tool to compare the universities. The last section is Visualization to visualize the data on Google Maps geographically so that it is easy for end users to locate the university. 3.1 Data Collector The Data Collector component is responsible for collecting data from the Universities/colleges web site . The Data Collector component is currently a manual process. We have to automate it in the next releases. We have collected data from UK universities’ websites. We have also collected RDF from RKB Explorer. The whole component in our architecture has been divided into the following sub-components 1) RDF Store 2) RDF Parser 3) List of Universities/Colleges. 3.1.1 RDF Store We have collected and stored UK universities and colleges' RDF from the Linked Open Data project . The RDF means Resource Description Framework which stores semantically rich resources. In our dataset, and understandable by machines in this RDF different kinds of information are stored and the structure is very complex and difficult for users to understand. In RDF Document holds the following properties figure-3. In our collected RDF the major attributes are the University Name, Number of research papers topic of the research papers, etc. To extract this information from the collected RDF files we wrote a script that is described in the next section. 3.1.2 RDF Parser RDF parser is a general type of script that takes RDF as input and populates the MySQL database. We need to define the script that which attributes need to be extracted from the RDF file. Therefore, our script loads in RDF from the RDF store, and for the mentioned attributes the data is populated in databases. The conversion reason is simply that we can play with relational databases in a lot of more different ways conveniently. 3.1.3 List of Universities/Colleges We have collected and stored all the UK universities and colleges data from UK universities/college websites . There are different kinds of information stored in this dataset. For example, how many students are there in the university? how many mature students? How many International students? Male/female percentage? How many Students are enrolled in different subjects? Etc. 3.2 University Data Files We have stored all the UK data in different files some data is stored in a Word file and some is stored in an Excel file the fee structure is stored and university faculty is stored in a Word file and other data and the university latitude and longitude are stored in an excel file and all the data collected from collector section in which all the UK universities and colleges data stored. 3.3 Mashup We have to use a mash-up section in which we combine all the data, visualize this data, and aggregate the data it is very important to make existing data more useful, and efficient moreover for personal and professional use. This section is further divided into different parts Pre-Processing 2) Data Populator 3) Database 4) Google Map API 5) Dojo (Pie chart) 3.3.1 Pre-Processing Real-world data are generally Incomplete lacking attribute values, lacking certain attributes of interest, or containing only aggregate data Noisy: containing errors or outliers Inconsistent: containing discrepancies in codes or names we are doing data preprocessing and Data cleaning and we have corrected and filled the missing values, smooth noisy data, identified or removed outliers, correct the given code given for universities in ascending order and resolved inconsistencies. Data is integrated using multiple files. 3.3.2 Data Populator The Data Populator Application extracts the available information from the Customized RDF file with the help of an RDF parser to populate the data and store it in the database. 3.3.3 Database In the database, all the data related to UK universities and colleges are stored. We export 1st all the data from an Excel file into a CSV file (which is generally a text file) and then import it into the MYSQL Database. 3.3.4 Google Map API The database provides the name of universities and cities and their latitude and longitude information. After that, we created a marker and placed the data geographically on the Map on the exact latitude and longitude. 3.3.5 Dojo (Pie Chart) Dojo Toolkit is an open-source modular JavaScript library (or more particularly JavaScript toolkit) designed to ease the speedy progress of cross-platform, JavaScript/Ajax-based applications and websites. For comparison of universities, we draw a pie chart in Dojo Tools to compare different universities. 4 Visualization Information visualization is the art of presenting data in a visual way that users can understand and enjoy. Dashboards, scatter plots, and Good Map API are common examples of information visualization. The basic purpose of Information visualization is to represent the data in a meaningful way that a user can understand better. Information visualization allows users to draw insights from abstract data efficiently and effectively. Information visualization plays an important role in making data more useful and turning unrefined information into actionable insights. 4.1 Overall Picture of The Decision Support System for University Selection The comprehensive and overall visualization of UK universities and colleges is presented in Figure 4, offering both an overview and a detailed view. Each university and college are accurately positioned on Google Maps based on their longitude and latitude coordinates. Users have the flexibility to select a specific institution by either using the mouse cursor or opting from the available options in the drop-down menu. Furthermore, for users who wish to search for a particular university, there is a search functionality provided in the drop-down menu, allowing them to easily locate and select their desired institution. To enhance accessibility to university and college information, we have implemented a highlighter feature within our Graphical User Interface (GUI), which enables users to easily identify and access specific sections of interest. Additionally, we have incorporated Zoom In functionality on Google Maps in Figure 5, enabling users to obtain a clearer understanding of the distribution and placement of universities and colleges. 4.2 Click on University or Search University from The Drop-Down Menu The Google Maps interface displays universities in a zoomable manner, as depicted in Figure 5. By clicking on any university, the user can access relevant information located at the top of the screen. This information includes the university's name and address, the total number of students, the breakdown of undergraduate students, the availability of sandwich programs, the presence of international and mature students, student placements abroad, male-to-female percentage, and the university's ranking. This wealth of information allows users to make informed decisions based on various criteria. Users can choose a university based on its proximity to their location, utilizing the address provided on Google Maps. Additionally, users can select a university based on specific parameters, such as the best option for them, as indicated by the ranking provided. To facilitate ease of use, a drop-down menu is available for users to quickly access detailed information about a specific university. This eliminates the need for users to individually search and click on multiple universities on Google Maps. By selecting their desired university from the drop-down menu, users can view the precise information they seek, mirroring the details presented in Figure 6. 4.3 Comparison of Two Universities with Different Parameters Figure 7 displays two drop-down menus allowing users to choose university X1 from one search box and university X2 from another. After making their selections, users can click on the "go and compare" option. First, they need to select the relevant parameters and check the corresponding checkboxes. Upon clicking "go & compare," Figure 7 presents a comparison between Oxford University and the University of Leeds based on different parameters, as per the user's request. Clicking on the "go & compare" option opens another window, shown in Figure 8, where two dojo pie charts present the information in distinct colors. When the cursor is placed over a color, it separates from the chart, which is known as hoaring. This allows users to obtain the desired information while hoaring over the chart. 4.4 Comparison of Two Universities with All Parameters When a user wishes to compare a university using all parameters, they will select one university from a dropdown list and another from a separate dropdown list. After checking all the parameters, they will click on the "go and compare" button. This action opens another window, depicted in Figure 8, where two dojo pie charts present the information using distinct colors. When the cursor is placed over a color, it separates from the charts, a phenomenon known as hoaring. This feature enables the user to obtain the desired information while hoaring over the chart. Figure 8 displays a comparison between the London School of Science and Technology and the European School of Economics. 4.5 Comparison with Other Systems By comparing the proposed system, and other well-known performance monitoring query resolve systems, our results in Table 1.1 aim to make comparisons. This overview may be useful in determining whether My System for Discovering Universities and their Visualization Based on User’s Preferences will meet a user’s needs and be useful in retrieving the user’s requested inquiry. 5 Conclusions Choosing the right university is a critical decision for both students and professors. It requires gathering and analyzing information about potential universities, which can be time-consuming and challenging, particularly when dealing with unstructured and semi-structured data online. In response to this challenge, a decision support system has been proposed that can collect information on universities from various sources, including their websites and online resources, and combine it based on multiple parameters such as research output, student strength, geographical location, and overall ranking. Moreover, the system automatically extracts useful information from semantic web technologies and datasets. The proposed system provides an intuitive visualization that enables users to rank universities based on their preferences and compare them comprehensively. The system was tested on data from 301 UK universities, and manual and automated methods were used to extract relevant information. The results showed that the proposed approach is effective in facilitating the university selection process for students and professors. The system can serve as a valuable tool for decision-making and support in the higher education sector. On the other hand, selecting a suitable university is a crucial decision for students and professors, which depends on various factors such as research environment, student strength, geographical location, and overall ranking of the university. However, searching for this information manually over the internet can be challenging due to the unstructured or semi-structured nature of online data. Typically, search engines and university-ranking databases are used to find this important information, but they often fail to answer specific queries. For instance, which university is ranked on the top for a typical parameter, or which university will suit a person based on his geographical location and preferences? There is no comprehensive system that can facilitate students and professors in accomplishing this important task. However, a proposed system that has been compared with other university systems found it to be the best in answering the aforementioned questions. The system can help users compare universities comprehensively and more effectively. It can be a valuable tool for decision-making and support in the higher education sector. Numerous other questions arise in the minds of students and professors when joining a particular university, and the proposed system can answer many of these queries. Declarations Author Contributions: Shabir Ali Shah gathered all the data from different sources and organized, developed an application for conducting experiments, interpreted results, and authored the paper. Malik Sikandar Hayat Khiyal and Mohammad Daud Awan served as corresponding authors, providing guidance and overseeing the research endeavors. Acknowledgment : The author would like to thank and express gratitude to all those who contributed to gathering and downloading the related data for this research Funding Statement: This study was not funded by anyone or by any organization (No grant). To cover the costs associated with publication, the author of an article accepted for publication in the Journal will pay an article-processing charge (APC). Conflicts of Interest: The authors hereby state that there are no conflicts of interest to disclose regarding the current study. Ethics Approval (Non-Biological or Non-Medical manuscripts do not need to write this part): This article does not contain any studies with animals performed by any of the authors. The authors extracted data with the assistance of domain experts. Data Availability and Materials: The corresponding author can provide the data supporting the findings of this study upon request. References Givens, M., Holdsworth, L., Mi, X., Rascoe, F., Valk, A., & Viars, K. E. (2020). Multimodal information literacy in higher education: Critical thinking, technology, and technical skill. In P. Sullivan, J. L. Lantz, & B. A. Sullivan (Eds.), Handbook of Research on Integrating Digital Technology With Literacy Pedagogies (pp. 97-120). IGI Global. https://doi.org/10.4018/978-1-7998-0246-4.ch005 Srinidhi, N. (2017). Intelligent information visualization system. Nanyang Technological University. https://dr.ntu.edu.sg//handle/10356/71930 Mohanasundaram, K., & Dharmendran, S. (2016). Study on factors determining the selection of Higher Educational Institutions after School among Students in India. Clear International Journal of Research in Commerce and Management, 7(10), 54-56. Marginson, S. (2006). Dynamics of National and Global Competition in Higher Education. Higher Education, 52(1), 1-39. Patton, H. L. (2000). How Administrators Can Influence Student University Selection Criteria. Higher Education in Europe, 25(3), 345-350. Fox, S., & Madden, M. (2008). Generations Online. Retrieved from www.pewinternet.org/pdfs/PIP Generations Memo.pdf Gomes, L., & Murphy, J. (2003). An Exploratory Study of Marketing International Education Online. The International Journal of Educational Management, 17(3), 116-125. Fifth Education in a Changing Environment Conference Book 2009: 'Critical Voices, Critical Times.' Webometrics. (2021). Countries arranged by Number of Universities in Top Ranks | Ranking Web of Universities: Webometrics ranks 30000 institutions. Retrieved from https://www.webometrics.info/en/distribution by country Chen, C. (2002). Information visualization. Information Visualization, 1(1), 1–4. Eick, S. G. (2005). Information visualization at 10. IEEE Comput Graph Appl, 25, 12–14. Khan, M. S., Kulathuramaiyer, N., & Maurer, H. (2008). Applications of Mash-ups for a Digital Journal. Journal of Universal Computer Science, 14(10), 1695-1716. Akbulut, M., & Çare, B. (n.d.). The Beytepe Campus Map: A Mashup Application. Department of Information Management, Hacettepe University, Ankara, Turkey. [email protected] , [email protected] . Cho, A. (2007). An introduction to mashups for health librarians. Journal of the Canadian Health Libraries Association/Journal de l'Association des bibliothèques de la santé du Canada, 28(1), 19-22. Liew, C. L., & Foo, S. (2001). Electronic Documents: What Lies Ahead? In Proc 4th International Conference on Asian Digital Libraries (ICADL 2001), Bangalore, India (pp. 88-105). Kulathuramaiyer, N. (2007). Mashups: Emerging Application Development Paradigm for a Digital Journal. Journal of Universal Computer Science, 13(4), 531-542. Singleton, A., Gibin, M., & Longley, P. (n.d.). Exploratory Cartographic Visualization of Health and Higher Education through Google Maps API. Department of Geography – Centre for Advanced Spatial Analysis, University College London, Pearson Building, Gower Street, London WC1E 6B. [email protected] , [email protected] , [email protected] . McKiernan, G. (2003). New Age Navigation: Innovative Information Interfaces for Electronic Journals. SL (The Serials Librarian), 45(2), 87-123. Maurer, H., Krottmaier, H., & Dreher, H. (2006). Important Aspects of Modern Digital Libraries. In Proc. International Conference on Digital Libraries (ICDL), New Delhi, India (pp. 843-855). TechSoup. (n.d.). Mashups: An Easy, Free Way to Create Custom Web Apps. Retrieved from www.techsoup.org/learningcenter/webbuilding/page5788.cfm?cg=searchterms&sg=mashups. Accessed on: [28/02/2024]. Wang, J. (2022). SOA-based Information Integration Platform for Educational Management Decision Support System. In Advanced Aspects of Computational Intelligence and Applications of Fuzzy Logic and Soft Computing, May 2022. https://doi.org/10.1155/2022/7553333. Alisan, Y., & Serin, F. (2021). A Computer Assisted Decision Support System for Education Planning. International Journal of Information Technology & Decision Making, 20(05), 1383–1407. https://doi.org/10.1142/s021962202150036x. Zhao, Y., & Shan, S. (2021). Online Learning Support Service System Architecture Based on Location Service Architecture. Mobile Information Systems, 2021, 1–11. https://doi.org/10.1155/2021/6663934. Wang, X., Schneider, H., & Walsh, K. R. (2020). A Predictive Analytics Approach to Building a Decision Support System for Improving Graduation Rates at a Four-Year College. Journal of Organizational and End User Computing, 32(4), 43–62. https://doi.org/10.4018/joeuc.2020100103. Zhang, Z., Zhao, Z., & Yeom, D.-S. (2020). Decision Tree Algorithm-Based Model and Computer Simulation for Evaluating the Effectiveness of Physical Education in Universities. Complexity, 2020, e8868793. https://doi.org/10.1155/2020/8868793. Hasic, F., De Smedt, J., Vanden Broucke, S., & Serral Asensio, E. (2020). Decision as a Service (DaaS): A Service-Oriented Architecture Approach for Decisions in Processes. IEEE Transactions on Services Computing, 1–1. https://doi.org/10.1109/tsc.2020.2965516. Almeida, F., & Amoedo, N. (2018). Decision Support System For Internship Management in Higher Education. International Journal of Information Systems and Social Change, 9(1), 40–57. https://doi.org/10.4018/ijissc.2018010103. Torrents Vilà, D., & Troiano, H. (2021). El riesgo estimado en las elecciones educativas y las diferencias según origen formativo familiar en la educación superior / Estimated Risk in Educational Decision-Making and Differences by Family Educational Background in Higher Education Choices, 174. https://doi.org/10.5477/cis/reis.174.147. Wang, X., Schneider, H., & Walsh, K. R. (2020). A Predictive Analytics Approach to Building a Decision Support System for Improving Graduation Rates at a Four-Year College. Journal of Organizational and End User Computing, 32(4), 43–62. https://doi.org/10.4018/joeuc.2020100103. Ponomarev, A., & Mustafin, N. (2021). Decision support systems configuration based on knowledge-driven automated service composition: requirements and conceptual model. Procedia Computer Science, 186, 654–660. https://doi.org/10.1016/j.procs.2021.04.213. Aminudin, N., et al. (2018). Higher Education Selection using Simple Additive Weighting. International Journal of Engineering & Technology, 7(2.27), 211. https://doi.org/10.14419/ijet.v7i2.27.11731. Fernandes, P., Pereira, C. S., & Barbosa, A. (2015). A decision support approach to automatic timetabling in higher education institutions. Journal of Scheduling, 19(3), 335–348. https://doi.org/10.1007/s10951-015-0435-z. Zhu, Y. (2018). A Data Driven Educational Decision Support System. International Journal of Emerging Technologies in Learning (iJET), 13(11), 4. https://doi.org/10.3991/ijet.v13i11.9582. Footnotes http://www.schoolfinder.com/ http://www.collegeview.com/articles/article/college-finder http://www.euniversityfinder.com/ http://unistats.direct.gov.uk/ http://www.vimo.com http://healthmap.org http://www.ucas.com/students/choosingcourses/choosinguni/instguide/ www.rkbeplorer.com http://www.ucas.com/students/choosingcourses/choosinguni/instguide/ Table Table 1.1 is available in the Supplementary Files section Additional Declarations No competing interests reported. Supplementary Files Table1.1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 21 Mar, 2024 Submission checks completed at journal 20 Mar, 2024 First submitted to journal 20 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4135589","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282069283,"identity":"60fb161a-d1f0-4d80-802a-848fb9bd7a43","order_by":0,"name":"Shabir Ali 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system\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4135589/v1/34b432a89877fc4979a12c0a.png"},{"id":53408830,"identity":"b25ea4f6-01a1-40fd-89f7-f8c543f09a09","added_by":"auto","created_at":"2024-03-25 16:04:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":267088,"visible":true,"origin":"","legend":"\u003cp\u003eSystem Architecture\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4135589/v1/6315ca7a7e3da2883f765b12.png"},{"id":53408832,"identity":"2271911d-4b87-4a3d-bb19-5557f1e85628","added_by":"auto","created_at":"2024-03-25 16:04:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":277844,"visible":true,"origin":"","legend":"\u003cp\u003eResource Description Framework (RDF) from Linked Open Data.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4135589/v1/5f2a46a007b7474cac0adee9.png"},{"id":53408831,"identity":"a4a1400b-51bb-4537-aae7-7b7a6fc52b54","added_by":"auto","created_at":"2024-03-25 16:04:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":665371,"visible":true,"origin":"","legend":"\u003cp\u003eThe overall picture of Visualization of UK universities/colleges\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4135589/v1/3d2457d587f97062f01a69f2.png"},{"id":53409775,"identity":"b5e22def-1494-4c6c-a287-9ea485314938","added_by":"auto","created_at":"2024-03-25 16:12:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1104905,"visible":true,"origin":"","legend":"\u003cp\u003eUniversities are shown in a Zooming way on Google Map\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4135589/v1/f67db2523538869c938d10b8.png"},{"id":53408837,"identity":"123bed31-94e7-4c32-9ec4-842f457efaaa","added_by":"auto","created_at":"2024-03-25 16:04:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":483812,"visible":true,"origin":"","legend":"\u003cp\u003eClicking or Searching any UK university from the drop-down menu on Google Maps.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4135589/v1/997fa17e4b4c07ec4d9a21ca.png"},{"id":53408835,"identity":"67bd4af1-ea09-4679-8afd-4a30548c38f0","added_by":"auto","created_at":"2024-03-25 16:04:34","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":52919,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of two universities/colleges\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4135589/v1/fed9c1ef8f675ba3bba78096.png"},{"id":53408833,"identity":"68ae673a-1d3f-457f-8c93-de3bb40844d0","added_by":"auto","created_at":"2024-03-25 16:04:33","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":132139,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of two universities/colleges with all parameters.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4135589/v1/a76b47dd4387bf858b75776b.png"},{"id":53410639,"identity":"c7675b3c-4203-4ec9-aa55-b378e46c90f7","added_by":"auto","created_at":"2024-03-25 16:20:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3335149,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4135589/v1/88c9d6f2-f1c5-462a-b514-b0d6a6984e3c.pdf"},{"id":53408834,"identity":"611b07bd-bca9-4444-b900-addc72b0bd57","added_by":"auto","created_at":"2024-03-25 16:04:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5683064,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4135589/v1/4167db0a07c48f7cedf60ab7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Semantic Web Technologies in Higher Education: A Decision Support System for University Selection","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe rapid advancement of computer visualization techniques, alongside the growth of virtual and augmented reality, has significantly enhanced the possibilities for data visualization and the creation of immersive virtual spaces for educational purposes. This technological progress offers promising avenues for the effective presentation of information and the creation of engaging environments that cater to the needs of the new generation. In today's digital age, the journey towards higher education is navigated through a vast sea of information, making the selection of a suitable university a daunting task for students, educators, and researchers alike.\u003c/p\u003e \u003cp\u003eWith over 31,097 universities worldwide, the task of selecting a university becomes even more daunting. This highlights the need for a decision support system specifically tailored for university selection, capable of integrating data from various sources into a single, user-friendly platform. Such a system can offer a holistic view of universities, encompassing academic programs, faculty, campus facilities, student satisfaction, and financial aid options, thereby streamlining the university selection process and saving valuable time for students.\u003c/p\u003e \u003cp\u003eBased on the aforementioned notion, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts key steps. Data Layer: This is the foundational layer where the data is collected from the RDF knowledge bases and websites. It includes metrics such as the number of papers published, faculty count, details about foreign students, the ratio of students supervised by teachers, and information on various academic programs (BS, Masters, PhD). Aggregator: This step combines and processes data from the data layer, making it more structured and meaningful for analysis. Mining Layer: At this level, advanced data processing techniques are applied to the aggregated data to extract valuable insights, patterns, and trends. Users: The final layer where the processed data is made available to various end-users, such as students (for making informed decisions about admissions), teachers (for identifying collaboration opportunities and funding sources), universities, and libraries. submission. It will speed up the review and typesetting process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the heart of our system lies the integration of advanced visualization tools and semantic web technologies. These components work synergistically to curate and present data from a wide array of online resources, including academic publications, university rankings, and student reviews. By transforming raw data into intuitive graphical representations, the system allows users to easily navigate through complex information landscapes, enabling a deeper understanding of each university's offerings, ethos, and performance metrics [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis paper introduces a groundbreaking decision support system that harnesses the power of visualization and semantic web technologies to streamline the university selection process. By aggregating data from various online sources, this system offers a comprehensive platform that not only simplifies data analysis but also enhances decision-making through intuitive, user-centric visualization tools. These tools enable stakeholders to compare universities across multiple parameters, including academic programs, research output, campus facilities, and overall rankings, tailored to their unique preferences and needs. With an emphasis on the diverse perspectives of its users, the system facilitates a more informed, efficient, and personalized approach to university selection. This innovative approach promises to transform the complex landscape of higher education decision-making, providing a valuable resource for prospective students, faculty members seeking collaborations, and parents alike, in their quest to find the best academic fit. The proposed system provides an intuitive visualization that enables users to rank universities based on their preferences and compare them comprehensively. The system was tested on data from 301 UK universities, and results showed that the proposed approach is effective in facilitating the university selection process for students and professors. The system can serve as a valuable tool for decision-making and support in the higher education sector.\u003c/p\u003e"},{"header":"2 Related Work","content":"\u003cp\u003eThe selection of a suitable university is an important decision for different people. Their decision is based on a number of factors such as research environment, student strength, geographical location, course selection, and the overall ranking of the university. These requirements are normally followed by surfacing across the search engines/web portals, with different query strings and having hundreds of thousands of pages which further require manual inspection. Recommender systems: Today, there are numerous recommender systems accessible in many different fields that make it easier for people to go about their daily lives. Information visualization techniques: Information visualization is a strategy for presenting data in a meaningful and illustrative manner that people can readily analyze and comprehend.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Online Systems/Web Portals\u003c/h2\u003e \u003cp\u003eA number of systems are available online that are being used by the community to find suitable universities/colleges. For example, the School Finder system is being used for Canadian colleges and schools since 1995. With the help of the School Finder\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e system users can find out about the graduate/Professional and undergraduate programs which are offered by the Canadian school and colleges. When anyone wants to find or search about the program then he will write the desired program in the search box and it will show a user/person of all the Schools/Colleges where the Programs/Education subjects are taught at all levels in the graduate, undergraduate, and secondary level. But there is no concept of visualization and comparison of these programs with each other and which one is the best school or college among all rank-wise. College Finder\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e provides information about thousands of schools/colleges all over Canada, in this system a person/user can use different parameters, and a person/user can also search about the school/college based upon his/her religious concept in which he/she is more interested. College Finder provides a search through their extensive database and finds colleges/universities with a person\u0026rsquo;s/user\u0026rsquo;s religious beliefs also. There are different parameters used for search criteria. The following criteria are used for searching 1) near to his location, 2) academic standards, 3) cost, 4) party scene, and 5) Greek life and majors offered. But there is no concept of visualization and it does not show us any comparison of different universities/colleges. In the online University Finder\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e system user can use the information to find their choice of university or college online in the United States. This system provides complete information about all the universities and colleges across the country which are existing in the United States. One can access this information at his/her fingertips if he/she is having a reliable internet service and also a system, financial aid, an academic background, and more athletics. This system also provides the comprehensive profiles of all universities in the United States. A person can receive information on obtaining a university degree online and one can sit at home, apply online to one of the best universities or colleges in the United States, study online, and then obtain an online degree. Online university finder systems can be used to find the users desired university or college online in the United States. The system provides complete information about all the universities and colleges across the country which are located in the United States. Online university finder system also gives student loans for study and one can search for the best university and cheap university for study. But there is no concept of visualization and compression of these universities with each other and which one is the best among these universities located in the United States. Unistats\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e is the official website of the UK. This system helps the user when anyone wants to apply to UK universities/colleges. On this website there are two types of search options for users 1) in which subject the user is interested to study? 2) In which university or college, the user is interested to apply? But again, it\u0026rsquo;s very difficult to see that which subject is taught in which university and then compare the universities with each other\u0026rsquo;s.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Visualization Techniques\u003c/h2\u003e \u003cp\u003eThe authors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] explain that WWW is currently experiencing revolutionary growth due to various emerging tools, techniques, and concepts. In this paper [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] the author also discovers and gives the solution to the problems while using an emerging Web 2.0 technology and he explores the application of mashups for the Journal of Universal Computer Science (JUCS) and encourages the readers, and authors to use that application. There is continuous improvement in Electronic journals and their services based on technological developments. Great effort has been made in providing high-level access options to the users of e-collections using 2D or 3D maps involving the semantic analysis and visualization of relevant topics [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Some important aspects of modern digital libraries like searching intelligently, to visualizing the search results, have been discussed in [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The author expands the publication of J.UCS and it is necessary to know about the current state of readership and accessibility of journals the author also determines which locations (cities and institutes) are contributing less, more, and which have stopped contributing [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Many universities developed web-based campus maps [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The spatial features of the geographical information system (GIS) are added in such types of maps for facility visual searches. The author discussed that the purpose of this paper is to design and build up an interactive, easy, and web-based Beytepe Campus Map to process visual queries on the geographical information system (GIS) and make it available through the Hacettepe University website, the main categories have also sub-categories to present the places at Beytepe Campus 1) Academic units,2) Administrative unit, 3) Sheltering, 4) Nutrition, 5) Health, 6) Transportation, 7) Entertainment, and 8) sports center. The author discussed Vimo in the integrated comparison portal released in January 2006. Vimo\u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e allows US rates, and purchases health insurance and health saving accounts, and also selects the physician profiles from across the US. Vimo can find physicians and compare prices of each hospital and also there is a facility to allow the users to read and post reviews. The author uses Google Map API\u003ca class=\"FNLink\" href=\"#Fn6\" id=\"#FNLinkFn6\"\u003e\u003c/a\u003e which searches the exact location of a health professional\u0026rsquo;s office when the consumer clicks on the name of Google Map, Health map brings different data sources together to complete a combined and comprehensive view of the present state of infectious diseases and also the effect of these diseases on human and the effect on animal health. Google Maps also provide the latitude and longitude to show the exact hospital location and address but the author has not mentioned here about which hospital is better than the other hospitals. The author [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] explains that JUCS is a unique electronic journal of computer science having more than Fifteen hundred (1500) research publication in different domains of computer science and added so many new ideas and features recently, which includes semantic searching as well as annotative option and collaborative option. JUCS is 1st electronic journal that implemented personal annotations and public annotations. JUCS also implemented research publication in multi-format options and define the category in multiples way etc. The author [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] also explored the distribution of authors who published papers in JUCS and editors across the world. Moreover, JUCS keeps up all the data for authors and editors which includes their country information, city information, and university information. The author 1st developed two (02) types of options. In 1st option visualization of author information with JUCS data on Google Map API. 2nd visualization of author distribution geographically and the Zooming option is also available. For full visualization, the author used manual effort and somewhere updating blank files for all this to be more effective automated technique is very much necessary. While using automated techniques one can better explore the city name and country name of the university in the search query Google Map API with author information. There is no comparison among authors, that shows users about the author\u0026rsquo;s publication that in which area he is so strong and how many publications he did in that category etc. Cartography [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] is the word used as the art and science of making maps and historically used by Geography as its language from this cartography gained new tools and media that enhanced the static maps and introduced multiple layers. The term Geographic Visualization or \u0026ldquo;GeoVisualization\u0026rdquo; (Gvis) refers to spatial data and can be used for all layers of problem-solving in geographical analysis. In the paper, the author presented two applications of Google Maps API in which health and higher education data overlaid as a thematic layer on top of the standard Google Maps base layer. The Data used to generate health applications result from registered patients within 48 General Practices in Southwark Primary Care Trust, situated in the region of Southwark, South London. These data are together through a system called Exeter and are used to monitor the practice activity against the Quality and Outcome Framework (QOF). The data used within the Higher Education (HE) mashup use an extract of the Universities and Colleges Admissions Service (UCAS) applicant database which was created during the 2004 application cycle. The author also discussed the advantages of data visualization and how to use different colors, fonts, and layouts while using data visualization techniques and with the help of data visualization, users avoid potential pitfalls [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The author explained that the Convenience store area is dependably the important feature, which is additionally a vital factor. Appropriate stores cannot just decide the number of store customers, numerous elements decide the accomplishment of a convenience store, and the nature of the store address assumes a key part in the achievement of the accommodation store. The author presented data visualization innovation and data recovery innovation in light of the investigation of the innovation of data visualization and geographic spatial metadata [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The creator additionally said that shading and geometry portrayals are quickly perceived by the human\u0026rsquo;s cerebrum, and information representation advances furnished by information mining come about with characteristic and natural activity interfaces. The fulfillment of the client\u0026rsquo;s request for such huge numbers of information mining emphasis was performed. The examination is as yet progressing and he is constantly upgrading and creating basic information mining calculation and information representation models in the framework [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The author discussed that the visualization technique is not a new concept but by using data visualization one can present the data graphically or pictorially and also discussed the free sources of data and the requirement of user-created content on social media have also led to rise in popularity of data visualization concept. The author also discussed the advantages of data visualization and how to use different colors, fonts, and layouts while using data visualization techniques and with the help of data visualization, users avoid potential pitfalls. Librarians and other information professionals are using data visualization to generate annual reports and insightful internal library appraisals. Library staff can also prepare themselves to teach and assist others in creating captivating data-driven visualization [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Decision Support Systems\u003c/h2\u003e \u003cp\u003eThe expansion of schools and the increasing number of campuses, departments, teachers, and students call for the development of an open and efficient online school administration system. The current resource management platform poses challenges for sharing educational resources due to its varied structures. This study [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] proposes an information integration platform based on Service-Oriented Architecture (SOA) to unify various enterprise application systems, enabling information sharing and meeting cross-departmental business needs. The proposed platform minimizes the impact of demand changes, enhances flexibility, and streamlines educational administration processes. By adopting SOA and Web services, the platform integrates existing information system resources, saves development costs, and improves performance management quality. The loosely coupled and reusable module design allows for seamless integration and reduces development complexity. The use of Web services technology simplifies system deployment and usage. It is expected that as the support for SOA advances, this SO-based information integration platform will find broader applications in enterprise informatization.\u003c/p\u003e \u003cp\u003eIn this paper, the author [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] explained that technological advances necessitate collaborative efforts among universities, teachers, and students to restructure departments and courses. Failing to do so risks reduced quality and competitiveness. To address this, a decision support system is proposed with three stages: data collection, conversion into meaningful information using natural language processing, and ranking alternatives using multi-criteria decision-making. This system benefits universities by informing department and course offerings, helping teachers create or shape courses, and guiding students in their choices. Experimental validation using computer engineering job postings and course contents from Turkish universities confirm the system's applicability and reliability.\u003c/p\u003e \u003cp\u003eThis study [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] focuses on developing an online learning support system using location-based service architecture. The research analyzes learning result data and implements an improved algorithm to enhance accuracy. The study concludes that the learning support service system plays a role in improving the quality of online education and advancing its development. By combining location-based service architecture with the learning system, the study introduces a new research direction. The algorithm improves content-based recommendations by incorporating weighted recommendation results based on geographic information, location preferences, and user decision-making. The system tracks students' real-time progress, provides personalized guidance, and effectively monitors learning progress and proficiency. The evaluation system considers students' achievements and abilities, offering scientific and personalized service. The learning support system employs big data, learning analysis, and mobile Internet technology to provide intelligent and humanized support services, while the application of location-based service architecture introduces a new evaluation and teaching method, quantifying learning states and reflecting learning effects comprehensively and objectively.\u003c/p\u003e \u003cp\u003eThe article [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] addresses the concern of low graduation rates at four-year state colleges, despite the use of academic indicators in the admission process. The authors suggest using an ensemble of analytic models that incorporate cost analysis to inform decision support systems. By analyzing ten years of data for 10,000 students and applying ten different models, the research aims to identify the best predictor of at-risk students. The study also utilizes the receiver operating characteristic curve to determine the optimal balance between false positive and false negative levels to achieve cost-effectiveness.\u003c/p\u003e \u003cp\u003eThis paper [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] focuses on analyzing students' physical education information, course exam results, and learning data from an online teaching platform using the forest algorithm and decision tree algorithm. The objective is to generate decision trees and classification rules to identify factors influencing students' physical education performance. By constructing a model for assessing teaching effectiveness, the study aims to improve teaching quality and strategies. The research includes data collection, preprocessing, model construction, algorithm optimization, and simulation results. The CART algorithm is specifically applied to analyze student data and predict their effectiveness in physical education. The study highlights the importance of effective teaching methods in e-learning platforms and suggests pedagogical adjustments based on the identified rules. Decision trees and random forests are chosen due to their clarity, simplicity, computational efficiency, and accuracy. The application of CART algorithms in assessing student effectiveness in physical education holds significance.\u003c/p\u003e \u003cp\u003eThis paper discusses [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] the separation of decision modeling from process modeling and introduces a Decision as a Service (DaaS) layered Service-Oriented Architecture (SOA). The DaaS approach treats decisions as automated and externalized services that processes can invoke on demand. The paper formalizes the DaaS framework using Decision Model and Notation (DMN) constructs and evaluates its adherence to SOA principles such as abstraction, reusability, and loose coupling. The benefits of the DaaS design on process-decision modeling and mining are discussed, and a real-life example of a bank loan application and approval process is used to illustrate the DaaS design. The paper contributes to the understanding of the interaction between decisions and processes and demonstrates the scalability, maintainability, flexibility, and understandability provided by the DaaS design. The proposed framework enhances integrated process-decision modeling and shows promising results in real-life event logs.\u003c/p\u003e \u003cp\u003eThe importance of successful internships for students' future careers is recognized, and a decision support model is proposed to enhance the assignment process in higher education. The model consists of seven phases, which can be extended to nine phases, including students' choice of internship place. The model is iterative and interactive, involving the course coordinator and students. Results from four scenarios validate the model, showing a high correlation between students and internship proposals. The proposed decision support system aims to complement the manual assignment process, which becomes challenging with a large number of students and proposals. The model incorporates objective and subjective evaluation elements to improve student and company satisfaction. However, limitations include the inability to measure the impact on student employability and the difficulty of quantifying soft skills. The model requires further testing in different scenarios and institutions [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study examines [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] the role of estimated risk in educational choices and its impact on educational inequalities, specifically focusing on social background differences. Using data from the ISCY Project in Barcelona, the study analyzes the estimated risk in higher education access. The findings reveal disparities in estimated risk based on social and economic factors. By operationalizing and contrasting the concept of estimated risk, the study demonstrates its usefulness as a framework for explaining educational inequalities and evaluating educational policies. Students' educational choices are influenced by their social background, leading to educational segmentation and the potential reproduction of social inequalities. The study explores the role of risk management in educational choices, considering factors such as motivations, academic abilities, and resources. Survey data are used to operationalize economic, academic, and social risks estimated by students and examine their relationship with actual choices.\u003c/p\u003e \u003cp\u003eDespite the ongoing concern about graduation rates at four-year state colleges, little improvement has been made in overall graduation rates. Academic indicators like high school GPA and ACT/SAT scores have long been used for selective admission, yet recent statistics indicate that less than 40% of students graduate within four years in the US. To address this issue, the authors propose an ensemble of analytic models that consider cost as a more effective approach for decision support systems. The study [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] analyzes ten years of data for 10,000 students and applies ten analytical models to identify at-risk students. By using the receiver operating characteristic curve, the research determines the optimal balance between false positive and false negative levels. Implementing a decision support system with predictive analytics can help identify at-risk students early on and implement interventions to prevent dropout. This approach enables administrators to make cost-effective decisions and utilize limited resources efficiently. By focusing on first and second-semester dropouts, timely decision-making and assessment of the effectiveness of administrative changes can be achieved. The article concludes with discussions and recommendations on modeling and practical applications within resource constraints.\u003c/p\u003e \u003cp\u003eThe paper [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] proposes an approach to building configurable service-oriented decision support systems through automated service composition, which simplifies the development process. The results presented include a functional framework for different types of decision support systems, requirements for configurable service-oriented systems and their components, and a conceptual model for such systems. This novel approach enables the development of problem-specific decision support systems that can be used with little or no special training, accelerating the development cycle. Future work involves encoding typical service compositions and creating a methodology for generating services as building blocks in these systems.\u003c/p\u003e \u003cp\u003eA decision support system was developed to assist the community in selecting a suitable college based on their capabilities and job demands. The system uses the Simple Additive Weighting (SAW) method to provide recommendations to users by considering predetermined criteria. The study [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] concludes that SAW is effective in solving the selection of universities problem, with accreditation being the most prioritized criterion. Suggestions for further improvement include exploring other decision-making methods, incorporating additional criteria, and utilizing a computer application to streamline the decision-making process.\u003c/p\u003e \u003cp\u003eAutomating and optimizing the creation of timetables for educational institutions is crucial to reduce costs. Previous studies on this problem have been based on unrealistic models with limited practical application. This paper [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] summarizes the work by Bullet Solutions, which focused on understanding and modeling the problem, developing robust algorithms, and employing optimization methods. The BTTE application, resulting from this work, achieved high-quality results with significant time savings (85%) in all analyzed cases. The application improved processes, centralizing and organizing information, increasing workflow efficiency, and aligning institutions with digital society procedures. The use of advanced technology for automation and optimization has enhanced the image and positioning of institutions while providing top management with greater control over teaching services. Notably, considerable savings in teacher hiring have been realized through the implementation of the BTTE application.\u003c/p\u003e \u003cp\u003eA study [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] was conducted to develop a Data-Driven Education Decision Support System (DDEDSS) as an innovative tool for educational decision-making. The DDEDSS software prototype was designed and tested using education data from two sessions. The system successfully evaluated learners' performance and provided a basis for curriculum optimization and class adjustments. The research demonstrated the significance of DDEDSS in educational research. The study focused on data acquisition, storage, integration, analysis, and mining, using SQL Server 2008 as the tool software. However, the development of the DDEDSS software faced challenges in utilizing built-in data analysis and mining functions. The study also identified the alignment between the five levels of data processing and the subject system's levels of practice, technology, science, sentiment, and philosophy, further validating the feasibility of the information and interaction system.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Proposed Technique","content":"\u003cp\u003eA Decision Support System for University Selection is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The overall architecture of the system is divided into four main layers such as 1) Data Collector, 2) University Data Files, 3) Mash-up, and 4) Visualization components. The Data Collector component consists of sub-components that load the RDF triplets from the RDF store, and convert them to MySQL database format for SQL queries in the future. The RDF store has various properties like the subject, author, etc. The RDF Parser converts it and stores it in the MySQL database. This conversion enables us to build a user-friendly interface containing a query posting mechanism and searches for a total number of papers published in the universities. This information is extracted from UK universities\u0026rsquo; RDF, next Data stored in files, next using pre-processing the data and finally organized the data and stored in the MySQL database and then using Google map API and use the dojo tool to compare the universities. The last section is Visualization to visualize the data on Google Maps geographically so that it is easy for end users to locate the university.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data Collector\u003c/h2\u003e \u003cp\u003eThe Data Collector component is responsible for collecting data from the Universities/colleges web site\u003ca class=\"FNLink\" href=\"#Fn7\" id=\"#FNLinkFn7\"\u003e\u003c/a\u003e. The Data Collector component is currently a manual process. We have to automate it in the next releases. We have collected data from UK universities\u0026rsquo; websites. We have also collected RDF from RKB Explorer. The whole component in our architecture has been divided into the following sub-components 1) RDF Store 2) RDF Parser 3) List of Universities/Colleges.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 RDF Store\u003c/h2\u003e \u003cp\u003eWe have collected and stored UK universities and colleges' RDF from the Linked Open Data project\u003ca class=\"FNLink\" href=\"#Fn8\" id=\"#FNLinkFn8\"\u003e\u003c/a\u003e. The RDF means Resource Description Framework which stores semantically rich resources. In our dataset, and understandable by machines in this RDF different kinds of information are stored and the structure is very complex and difficult for users to understand. In RDF Document holds the following properties figure-3. In our collected RDF the major attributes are the University Name, Number of research papers topic of the research papers, etc. To extract this information from the collected RDF files we wrote a script that is described in the next section.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 RDF Parser\u003c/h2\u003e \u003cp\u003eRDF parser is a general type of script that takes RDF as input and populates the MySQL database. We need to define the script that which attributes need to be extracted from the RDF file. Therefore, our script loads in RDF from the RDF store, and for the mentioned attributes the data is populated in databases. The conversion reason is simply that we can play with relational databases in a lot of more different ways conveniently.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 List of Universities/Colleges\u003c/h2\u003e \u003cp\u003eWe have collected and stored all the UK universities and colleges data from UK universities/college websites\u003ca class=\"FNLink\" href=\"#Fn9\" id=\"#FNLinkFn9\"\u003e\u003c/a\u003e. There are different kinds of information stored in this dataset. For example, how many students are there in the university? how many mature students? How many International students? Male/female percentage? How many Students are enrolled in different subjects? Etc.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 University Data Files\u003c/h2\u003e \u003cp\u003eWe have stored all the UK data in different files some data is stored in a Word file and some is stored in an Excel file the fee structure is stored and university faculty is stored in a Word file and other data and the university latitude and longitude are stored in an excel file and all the data collected from collector section in which all the UK universities and colleges data stored.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Mashup\u003c/h2\u003e \u003cp\u003eWe have to use a mash-up section in which we combine all the data, visualize this data, and aggregate the data it is very important to make existing data more useful, and efficient moreover for personal and professional use. This section is further divided into different parts\u003c/p\u003e \u003cp\u003ePre-Processing 2) Data Populator 3) Database 4) Google Map API 5) Dojo (Pie chart)\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Pre-Processing\u003c/h2\u003e \u003cp\u003eReal-world data are generally Incomplete lacking attribute values, lacking certain attributes of interest, or containing only aggregate data Noisy: containing errors or outliers Inconsistent: containing discrepancies in codes or names we are doing data preprocessing and Data cleaning and we have corrected and filled the missing values, smooth noisy data, identified or removed outliers, correct the given code given for universities in ascending order and resolved inconsistencies. Data is integrated using multiple files.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Data Populator\u003c/h2\u003e \u003cp\u003eThe Data Populator Application extracts the available information from the\u003c/p\u003e \u003cp\u003eCustomized RDF file with the help of an RDF parser to populate the data and store it in the database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Database\u003c/h2\u003e \u003cp\u003eIn the database, all the data related to UK universities and colleges are stored. We export 1st all the data from an Excel file into a CSV file (which is generally a text file) and then import it into the MYSQL Database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4 Google Map API\u003c/h2\u003e \u003cp\u003eThe database provides the name of universities and cities and their latitude and longitude information. After that, we created a marker and placed the data geographically on the Map on the exact latitude and longitude.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.3.5 Dojo (Pie Chart)\u003c/h2\u003e \u003cp\u003eDojo Toolkit is an open-source modular JavaScript library (or more particularly JavaScript toolkit) designed to ease the speedy progress of cross-platform, JavaScript/Ajax-based applications and websites. For comparison of universities, we draw a pie chart in Dojo Tools to compare different universities.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Visualization","content":"\u003cp\u003eInformation visualization is the art of presenting data in a visual way that users can understand and enjoy. Dashboards, scatter plots, and Good Map API are common examples of information visualization. The basic purpose of Information visualization is to represent the data in a meaningful way that a user can understand better. Information visualization allows users to draw insights from abstract data efficiently and effectively. Information visualization plays an important role in making data more useful and turning unrefined information into actionable insights.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 Overall Picture of The Decision Support System for University Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe comprehensive and overall visualization of UK universities and colleges is presented in Figure 4, offering both an overview and a detailed view. Each university and college are accurately positioned on Google Maps based on their longitude and latitude coordinates. Users have the flexibility to select a specific institution by either using the mouse cursor or opting from the available options in the drop-down menu. Furthermore, for users who wish to search for a particular university, there is a search functionality provided in the drop-down menu, allowing them to easily locate and select their desired institution. To enhance accessibility to university and college information, we have implemented a highlighter feature within our Graphical User Interface (GUI), which enables users to easily identify and access specific sections of interest. Additionally, we have incorporated Zoom In functionality on Google Maps in Figure 5, enabling users to obtain a clearer understanding of the distribution and placement of universities and colleges.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Click on University or Search University from The Drop-Down Menu\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Google Maps interface displays universities in a zoomable manner, as depicted in Figure 5. By clicking on any university, the user can access relevant information located at the top of the screen. This information includes the university\u0026apos;s name and address, the total number of students, the breakdown of undergraduate students, the availability of sandwich programs, the presence of international and mature students, student placements abroad, male-to-female percentage, and the university\u0026apos;s ranking. This wealth of information allows users to make informed decisions based on various criteria. Users can choose a university based on its proximity to their location, utilizing the address provided on Google Maps. Additionally, users can select a university based on specific parameters, such as the best option for them, as indicated by the ranking provided.\u003c/p\u003e\n\u003cp\u003eTo facilitate ease of use, a drop-down menu is available for users to quickly access detailed information about a specific university. This eliminates the need for users to individually search and click on multiple universities on Google Maps. By selecting their desired university from the drop-down menu, users can view the precise information they seek, mirroring the details presented in Figure 6.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Comparison of Two Universities with Different Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 7 displays two drop-down menus allowing users to choose university X1 from one search box and university X2 from another. After making their selections, users can click on the \u0026quot;go and compare\u0026quot; option. First, they need to select the relevant parameters and check the corresponding checkboxes. Upon clicking \u0026quot;go \u0026amp; compare,\u0026quot; Figure 7 presents a comparison between Oxford University and the University of Leeds based on different parameters, as per the user\u0026apos;s request. Clicking on the \u0026quot;go \u0026amp; compare\u0026quot; option opens another window, shown in Figure 8, where two dojo pie charts present the information in distinct colors. When the cursor is placed over a color, it separates from the chart, which is known as hoaring. This allows users to obtain the desired information while hoaring over the chart.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Comparison of Two Universities with All Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen a user wishes to compare a university using all parameters, they will select one university from a dropdown list and another from a separate dropdown list. After checking all the parameters, they will click on the \u0026quot;go and compare\u0026quot; button. This action opens another window, depicted in Figure 8, where two dojo pie charts present the information using distinct colors. When the cursor is placed over a color, it separates from the charts, a phenomenon known as hoaring. This feature enables the user to obtain the desired information while hoaring over the chart. Figure 8 displays a comparison between the London School of Science and Technology and the European School of Economics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Comparison with Other Systems\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy comparing the proposed system, and other well-known performance monitoring query resolve systems, our results in Table 1.1 aim to make comparisons. This overview may be useful in determining whether My System for Discovering Universities and their Visualization Based on User\u0026rsquo;s Preferences will meet a user\u0026rsquo;s needs and be useful in retrieving the user\u0026rsquo;s requested inquiry.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eChoosing the right university is a critical decision for both students and professors. It requires gathering and analyzing information about potential universities, which can be time-consuming and challenging, particularly when dealing with unstructured and semi-structured data online. In response to this challenge, a decision support system has been proposed that can collect information on universities from various sources, including their websites and online resources, and combine it based on multiple parameters such as research output, student strength, geographical location, and overall ranking. Moreover, the system automatically extracts useful information from semantic web technologies and datasets.\u003c/p\u003e\n\u003cp\u003eThe proposed system provides an intuitive visualization that enables users to rank universities based on their preferences and compare them comprehensively. The system was tested on data from 301 UK universities, and manual and automated methods were used to extract relevant information. The results showed that the proposed approach is effective in facilitating the university selection process for students and professors. The system can serve as a valuable tool for decision-making and support in the higher education sector.\u003c/p\u003e\n\u003cp\u003eOn the other hand, selecting a suitable university is a crucial decision for students and professors, which depends on various factors such as research environment, student strength, geographical location, and overall ranking of the university.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, searching for this information manually over the internet can be challenging due to the unstructured or semi-structured nature of online data. Typically, search engines and university-ranking databases are used to find this important information, but they often fail to answer specific queries. For instance, which university is ranked on the top for a typical parameter, or which university will suit a person based on his geographical location and preferences? There is no comprehensive system that can facilitate students and professors in accomplishing this important task.\u003c/p\u003e\n\u003cp\u003eHowever, a proposed system that has been compared with other university systems found it to be the best in answering the aforementioned questions. The system can help users compare universities comprehensively and more effectively. It can be a valuable tool for decision-making and support in the higher education sector. Numerous other questions arise in the minds of students and professors when joining a particular university, and the proposed system can answer many of these queries.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Shabir Ali Shah gathered all the data from different sources and organized, developed an application for conducting experiments, interpreted results, and authored the paper. Malik Sikandar Hayat Khiyal and Mohammad Daud Awan served as corresponding authors, providing guidance and overseeing the research endeavors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The author would like to thank and express gratitude to all those who contributed to gathering and downloading the related data for this research\u003c/p\u003e\n\u003cp\u003eFunding Statement:\u0026nbsp;This study was not funded by anyone or by any organization (No grant). To cover the costs associated with publication, the author of an article accepted for publication in the Journal will pay an article-processing charge (APC).\u003c/p\u003e\n\u003cp\u003eConflicts of Interest:\u0026nbsp;The authors hereby state that there are no conflicts of interest to disclose regarding the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval (Non-Biological or Non-Medical manuscripts do not need to write this part):\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with animals performed by any of the authors. The authors extracted data with the assistance of domain experts.\u003c/p\u003e\n\u003cp\u003eData Availability and Materials:\u0026nbsp;The corresponding author can provide the data supporting the findings of this study upon request.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGivens, M., Holdsworth, L., Mi, X., Rascoe, F., Valk, A., \u0026amp; Viars, K. E. (2020). Multimodal information literacy in higher education: Critical thinking, technology, and technical skill. In P. Sullivan, J. L. Lantz, \u0026amp; B. A. Sullivan (Eds.), Handbook of Research on Integrating Digital Technology With Literacy Pedagogies (pp. 97-120). IGI Global. https://doi.org/10.4018/978-1-7998-0246-4.ch005\u003c/li\u003e\n \u003cli\u003eSrinidhi, N. (2017). Intelligent information visualization system. Nanyang Technological University. https://dr.ntu.edu.sg//handle/10356/71930\u003c/li\u003e\n \u003cli\u003eMohanasundaram, K., \u0026amp; Dharmendran, S. (2016). Study on factors determining the selection of Higher Educational Institutions after School among Students in India. Clear International Journal of Research in Commerce and Management, 7(10), 54-56.\u003c/li\u003e\n \u003cli\u003eMarginson, S. (2006). Dynamics of National and Global Competition in Higher Education. Higher Education, 52(1), 1-39.\u003c/li\u003e\n \u003cli\u003ePatton, H. L. (2000). How Administrators Can Influence Student University Selection Criteria. Higher Education in Europe, 25(3), 345-350.\u003c/li\u003e\n \u003cli\u003eFox, S., \u0026amp; Madden, M. (2008). Generations Online. Retrieved from www.pewinternet.org/pdfs/PIP Generations Memo.pdf\u003c/li\u003e\n \u003cli\u003eGomes, L., \u0026amp; Murphy, J. (2003). An Exploratory Study of Marketing International Education Online. The International Journal of Educational Management, 17(3), 116-125.\u003c/li\u003e\n \u003cli\u003eFifth Education in a Changing Environment Conference Book 2009: \u0026apos;Critical Voices, Critical Times.\u0026apos;\u003c/li\u003e\n \u003cli\u003eWebometrics. (2021). Countries arranged by Number of Universities in Top Ranks | Ranking Web of Universities: Webometrics ranks 30000 institutions. Retrieved from https://www.webometrics.info/en/distribution by country\u003c/li\u003e\n \u003cli\u003eChen, C. (2002). Information visualization. Information Visualization, 1(1), 1\u0026ndash;4.\u003c/li\u003e\n \u003cli\u003eEick, S. G. (2005). Information visualization at 10. IEEE Comput Graph Appl, 25, 12\u0026ndash;14.\u003c/li\u003e\n \u003cli\u003eKhan, M. S., Kulathuramaiyer, N., \u0026amp; Maurer, H. (2008). Applications of Mash-ups for a Digital Journal. Journal of Universal Computer Science, 14(10), 1695-1716.\u003c/li\u003e\n \u003cli\u003eAkbulut, M., \u0026amp; \u0026Ccedil;are, B. (n.d.). The Beytepe Campus Map: A Mashup Application. Department of Information Management, Hacettepe University, Ankara, Turkey. [email protected], [email protected].\u003c/li\u003e\n \u003cli\u003eCho, A. (2007). An introduction to mashups for health librarians. Journal of the Canadian Health Libraries Association/Journal de l\u0026apos;Association des biblioth\u0026egrave;ques de la sant\u0026eacute; du Canada, 28(1), 19-22.\u003c/li\u003e\n \u003cli\u003eLiew, C. L., \u0026amp; Foo, S. (2001). Electronic Documents: What Lies Ahead? In Proc 4th International Conference on Asian Digital Libraries (ICADL 2001), Bangalore, India (pp. 88-105).\u003c/li\u003e\n \u003cli\u003eKulathuramaiyer, N. (2007). Mashups: Emerging Application Development Paradigm for a Digital Journal. Journal of Universal Computer Science, 13(4), 531-542.\u003c/li\u003e\n \u003cli\u003eSingleton, A., Gibin, M., \u0026amp; Longley, P. (n.d.). Exploratory Cartographic Visualization of Health and Higher Education through Google Maps API. Department of Geography \u0026ndash; Centre for Advanced Spatial Analysis, University College London, Pearson Building, Gower Street, London WC1E 6B. [email protected], [email protected], [email protected].\u003c/li\u003e\n \u003cli\u003eMcKiernan, G. (2003). New Age Navigation: Innovative Information Interfaces for Electronic Journals. SL (The Serials Librarian), 45(2), 87-123.\u003c/li\u003e\n \u003cli\u003eMaurer, H., Krottmaier, H., \u0026amp; Dreher, H. (2006). Important Aspects of Modern Digital Libraries. In Proc. International Conference on Digital Libraries (ICDL), New Delhi, India (pp. 843-855).\u003c/li\u003e\n \u003cli\u003eTechSoup. (n.d.). Mashups: An Easy, Free Way to Create Custom Web Apps. Retrieved from www.techsoup.org/learningcenter/webbuilding/page5788.cfm?cg=searchterms\u0026amp;sg=mashups. Accessed on: [28/02/2024].\u003c/li\u003e\n \u003cli\u003eWang, J. (2022). SOA-based Information Integration Platform for Educational Management Decision Support System. In Advanced Aspects of Computational Intelligence and Applications of Fuzzy Logic and Soft Computing, May 2022. https://doi.org/10.1155/2022/7553333.\u003c/li\u003e\n \u003cli\u003eAlisan, Y., \u0026amp; Serin, F. (2021). A Computer Assisted Decision Support System for Education Planning. International Journal of Information Technology \u0026amp; Decision Making, 20(05), 1383\u0026ndash;1407. https://doi.org/10.1142/s021962202150036x.\u003c/li\u003e\n \u003cli\u003eZhao, Y., \u0026amp; Shan, S. (2021). Online Learning Support Service System Architecture Based on Location Service Architecture. Mobile Information Systems, 2021, 1\u0026ndash;11. https://doi.org/10.1155/2021/6663934.\u003c/li\u003e\n \u003cli\u003eWang, X., Schneider, H., \u0026amp; Walsh, K. R. (2020). A Predictive Analytics Approach to Building a Decision Support System for Improving Graduation Rates at a Four-Year College. Journal of Organizational and End User Computing, 32(4), 43\u0026ndash;62. https://doi.org/10.4018/joeuc.2020100103.\u003c/li\u003e\n \u003cli\u003eZhang, Z., Zhao, Z., \u0026amp; Yeom, D.-S. (2020). Decision Tree Algorithm-Based Model and Computer Simulation for Evaluating the Effectiveness of Physical Education in Universities. Complexity, 2020, e8868793. https://doi.org/10.1155/2020/8868793.\u003c/li\u003e\n \u003cli\u003eHasic, F., De Smedt, J., Vanden Broucke, S., \u0026amp; Serral Asensio, E. (2020). Decision as a Service (DaaS): A Service-Oriented Architecture Approach for Decisions in Processes. IEEE Transactions on Services Computing, 1\u0026ndash;1. https://doi.org/10.1109/tsc.2020.2965516.\u003c/li\u003e\n \u003cli\u003eAlmeida, F., \u0026amp; Amoedo, N. (2018). Decision Support System For Internship Management in Higher Education. International Journal of Information Systems and Social Change, 9(1), 40\u0026ndash;57. https://doi.org/10.4018/ijissc.2018010103.\u003c/li\u003e\n \u003cli\u003eTorrents Vil\u0026agrave;, D., \u0026amp; Troiano, H. (2021). El riesgo estimado en las elecciones educativas y las diferencias seg\u0026uacute;n origen formativo familiar en la educaci\u0026oacute;n superior / Estimated Risk in Educational Decision-Making and Differences by Family Educational Background in Higher Education Choices, 174. https://doi.org/10.5477/cis/reis.174.147.\u003c/li\u003e\n \u003cli\u003eWang, X., Schneider, H., \u0026amp; Walsh, K. R. (2020). A Predictive Analytics Approach to Building a Decision Support System for Improving Graduation Rates at a Four-Year College. Journal of Organizational and End User Computing, 32(4), 43\u0026ndash;62. https://doi.org/10.4018/joeuc.2020100103.\u003c/li\u003e\n \u003cli\u003ePonomarev, A., \u0026amp; Mustafin, N. (2021). Decision support systems configuration based on knowledge-driven automated service composition: requirements and conceptual model. Procedia Computer Science, 186, 654\u0026ndash;660. https://doi.org/10.1016/j.procs.2021.04.213.\u003c/li\u003e\n \u003cli\u003eAminudin, N., et al. (2018). Higher Education Selection using Simple Additive Weighting. International Journal of Engineering \u0026amp; Technology, 7(2.27), 211. https://doi.org/10.14419/ijet.v7i2.27.11731.\u003c/li\u003e\n \u003cli\u003eFernandes, P., Pereira, C. S., \u0026amp; Barbosa, A. (2015). A decision support approach to automatic timetabling in higher education institutions. Journal of Scheduling, 19(3), 335\u0026ndash;348. https://doi.org/10.1007/s10951-015-0435-z.\u003c/li\u003e\n \u003cli\u003eZhu, Y. (2018). A Data Driven Educational Decision Support System. International Journal of Emerging Technologies in Learning (iJET), 13(11), 4. https://doi.org/10.3991/ijet.v13i11.9582.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.schoolfinder.com/\u003c/span\u003e\u003cspan address=\"http://www.schoolfinder.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.collegeview.com/articles/article/college-finder\u003c/span\u003e\u003cspan address=\"http://www.collegeview.com/articles/article/college-finder\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.euniversityfinder.com/\u003c/span\u003e\u003cspan address=\"http://www.euniversityfinder.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://unistats.direct.gov.uk/\u003c/span\u003e\u003cspan address=\"http://unistats.direct.gov.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.vimo.com\u003c/span\u003e\u003cspan address=\"http://www.vimo.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://healthmap.org\u003c/span\u003e\u003cspan address=\"http://healthmap.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ucas.com/students/choosingcourses/choosinguni/instguide/\u003c/span\u003e\u003cspan address=\"http://www.ucas.com/students/choosingcourses/choosinguni/instguide/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003cspan\u003ewww.rkbeplorer.com\u003c/span\u003e\u003c/span\u003e\u003cspan address=\"http://www.rkbeplorer.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ucas.com/students/choosingcourses/choosinguni/instguide/\u003c/span\u003e\u003cspan address=\"http://www.ucas.com/students/choosingcourses/choosinguni/instguide/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1.1 is available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"granular-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"grco","sideBox":"Learn more about [Granular Computing](http://link.springer.com/journal/41066)","snPcode":"41066","submissionUrl":"https://submission.nature.com/new-submission/41066/3","title":"Granular Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Information aggregation, Information visualization, Semantic web, Information retrieval, Decision Support System","lastPublishedDoi":"10.21203/rs.3.rs-4135589/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4135589/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe process of choosing an appropriate university is a significant and complex decision for both students and academics. The challenge lies in efficiently navigating and interpreting the vast amount of unstructured and semi-structured data available online. To tackle this issue, this paper introduces a sophisticated decision support system designed to aggregate and integrate data from diverse sources, such as university websites and other online platforms. This system employs semantic web technologies and various datasets for automatic information extraction, facilitating a more streamlined data analysis process. Key attributes such as research output, student demographics, geographical location, and overall rankings are utilized as parameters for data integration. The system is equipped with a user-friendly interface that offers customizable visualization tools. These tools enable users to prioritize and compare universities based on their individual preferences effectively. For empirical validation, we gathered and analyzed data from 301 universities in the United Kingdom, employing both manual and automated techniques for information extraction. The outcomes of this study underscore the efficiency and practicality of our approach in simplifying the university selection process for potential students and faculty members. Overall, this system presents itself as an invaluable resource for informed decision-making in the domain of higher education.\u003c/p\u003e","manuscriptTitle":"Integrating Semantic Web Technologies in Higher Education: A Decision Support System for University Selection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-25 16:04:28","doi":"10.21203/rs.3.rs-4135589/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-03-21T04:01:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-21T03:29:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Granular Computing","date":"2024-03-20T08:45:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"granular-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"grco","sideBox":"Learn more about [Granular Computing](http://link.springer.com/journal/41066)","snPcode":"41066","submissionUrl":"https://submission.nature.com/new-submission/41066/3","title":"Granular Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3a06522a-669a-498e-ae05-c6495e638e23","owner":[],"postedDate":"March 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-06T07:31:54+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-25 16:04:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4135589","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4135589","identity":"rs-4135589","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}