Leveraging AI Hyperparameters for Enhanced Security in Triplestore Databases: A Comprehensive Analysis of Deployment and Delivery Models

Leveraging AI Hyperparameters for Enhanced Security in Triplestore Databases: A Comprehensive Analysis of Deployment and Delivery Models | 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 Article Leveraging AI Hyperparameters for Enhanced Security in Triplestore Databases: A Comprehensive Analysis of Deployment and Delivery Models Mourad Henchiri, Sharyar Wani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4142144/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Leveraging ameliorated AI hyperparameters introduces a multifaceted research landscape that enhances the fundamental principles of optimization and security in triplestore databases. Yet, the implementation should align with the characteristics and requirements of triplestore technology. xVelocity and .NET Security can be implemented in Triplestore Databases to optimize performance and ensure security. The integration of xVelocity technology, which is known for its in-memory data compression and columnstore indexing capabilities, can enhance query performance in Triplestore Databases. Additionally, .NET Security measures can be applied to safeguard the database environment, ensuring secure data access and preventing potential vulnerabilities. Combining these technologies contributes to a robust and efficient Triplestore Database system. This research addresses key challenges associated with dynamic optimization strategies optimizing query performance, enhancing security measures, and integrating machine learning models within triplestore environments. Security considerations involve AI-driven access controls, anomaly detection, and ethical AI usage, ensuring dynamic and context-aware security policies. The research delves into machine learning model integration, focusing on model explainability, real-time updates, and the seamless interoperability of AI modules with triplestore databases. ML AI Triplestore database system performance metrics AI Hyperparameters Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-4142144","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":286995682,"identity":"5c37f7d2-9cda-458e-92d7-6805f583006f","order_by":0,"name":"Mourad Henchiri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYNACNhsGBmYIk7GBSC1ppGs5DGcS1qLb3mP24UfZ+cTt7OzPJH4w2MhuOMB8dAM+LWZnzhjP7Dl3O3FnM0OaZA9DmvGGA2xpN/BquZFjzMDbdjtxw2GGYzd4GA4nbjjAY0ZQC+PftnNALYxtN/8w/CdOCzNv2wGgFma22zwMB4jQcuZYMbPMuWTjDYfZ2H/LGCQbzzxMyC/Hmzczvimzk91w/vhjwzcVdrJ9x5uP4dWCBgwY4HE6CkbBKBgFo4ACAAB1TE8FqwGNZgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Nizwa","correspondingAuthor":true,"prefix":"","firstName":"Mourad","middleName":"","lastName":"Henchiri","suffix":""},{"id":286995685,"identity":"46b61b9e-91d4-4edc-9e89-34960636e320","order_by":1,"name":"Sharyar Wani","email":"","orcid":"","institution":"International Islamic University Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Sharyar","middleName":"","lastName":"Wani","suffix":""}],"badges":[],"createdAt":"2024-03-21 09:05:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4142144/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4142144/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57750610,"identity":"0511372f-e781-4e49-8dc3-017ce675a318","added_by":"auto","created_at":"2024-06-05 07:00:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":228104,"visible":true,"origin":"","legend":"","description":"","filename":"SPARQL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4142144/v1_covered_9ced3a00-e8e9-479b-86af-4a6417ddaff1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Leveraging AI Hyperparameters for Enhanced Security in Triplestore Databases: A Comprehensive Analysis of Deployment and Delivery Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ML, AI, Triplestore database system, performance metrics, AI Hyperparameters","lastPublishedDoi":"10.21203/rs.3.rs-4142144/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4142144/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLeveraging ameliorated AI hyperparameters introduces a multifaceted research landscape that enhances the fundamental principles of optimization and security in triplestore databases. 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