Implementing Integrity Assurance System for Big Data

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This paper proposes a data integrity assurance system for big data validation, assessing data fields, measurements, and compatibility with the data cycle using Python.

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This paper studies the implementation of a framework for ensuring “integrity” (data validity) in big data, focusing on validating the validity of data fields and measurements and assessing compatibility across a data processing cycle. The authors describe a model for big data integrity that evaluates both processing speed and verification accuracy, and they implement it using Python with real test data. A key caveat stated in the publication context is that the work was a preprint and is described as not peer reviewed at the time of that version. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract With the rapid advancement of big data technology and statistical data analysis solutions, the computing of big data and its services has become the subject of research and popular applications. There are many problems related to data quality that lead to making wrong decisions in institutions and companies. Current research rarely discusses how to validate data effectively to ensure its quality Integrity is data validity. It is a task that is not an easy task that is usually undertaken in national statistical organizations and institutions. There is an urgent need to produce a general framework to verify the integrity of big data. This methodology has been devoted to proposing a model that works on data integrity, especially big data, and how to address the validation process. The data also includes the validity of the data fields and the validity of measuring the data and assessing the compatibility with the data cycle chain. The speed of the processing process and the accuracy of the verification process for the integrity of big data are considered. Based on using the latest technologies and programming languages, the research was based on the programming language in Python and real test data.
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Implementing Integrity Assurance System for Big Data | 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 Implementing Integrity Assurance System for Big Data Fawaz Alyami, Saad Almutairi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-485987/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Aug, 2021 Read the published version in Wireless Personal Communications → Version 1 posted 4 You are reading this latest preprint version Abstract With the rapid advancement of big data technology and statistical data analysis solutions, the computing of big data and its services has become the subject of research and popular applications. There are many problems related to data quality that lead to making wrong decisions in institutions and companies. Current research rarely discusses how to validate data effectively to ensure its quality Integrity is data validity. It is a task that is not an easy task that is usually undertaken in national statistical organizations and institutions. There is an urgent need to produce a general framework to verify the integrity of big data. This methodology has been devoted to proposing a model that works on data integrity, especially big data, and how to address the validation process. The data also includes the validity of the data fields and the validity of measuring the data and assessing the compatibility with the data cycle chain. The speed of the processing process and the accuracy of the verification process for the integrity of big data are considered. Based on using the latest technologies and programming languages, the research was based on the programming language in Python and real test data. Cell Communication and Signaling Big Data Integrity Volume Data provenance Velocity Decisions V dimensions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Full Text Supplementary Files AuthorInformations.docx Cite Share Download PDF Status: Published Journal Publication published 24 Aug, 2021 Read the published version in Wireless Personal Communications → Version 1 posted Editorial decision: Minor revisions 23 Jun, 2021 Reviews received at journal 01 Jun, 2021 Reviewers invited by journal 31 May, 2021 First submitted to journal 30 Apr, 2021 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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