Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things | 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 Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things Aminollah Mahabadi, Faezeh Abdolazimi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4353622/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 The classification of large image data using traditional methods has been based on changes in image gray features, extraction of edge and contour feature information, or conversion between image coordinate sets. However, due to the growth of new image size and big image data (BID) in real-time multimedia communication and future online big data (BD) applications, these methods have become increasingly complex and resulting in poor real-time performance. These complex methods suffer from complex algorithms, massive data communication, slow processing speed, unintelligent predictive modeling, weak data classification, limited accuracy, uncleaned usage data, combined destructive artificial and natural noise, exchanging data values over time, and exhausting updating data for accurate operation of media storage. In the era of the Internet of Multimedia Things (IoMT), most modern devices can accurately capture vast amounts of observational data in the form of valuable images, text, and acoustic recordings. This reliable data must be kept valid for local data processing at different times and for globally extracting knowledge from information at various data levels to address forthcoming challenges related to big image data. The challenges of secure communication require accurate, reliable, fast, and effective information gathering for local decision-making and global knowledge mining of BID to support cyber-physical systems for speedy data virtualization of smart city devices. Complex and uncontrollable problems persist on the outskirts of grids, fogs, and cloud networks regarding data cleansing and privacy protection for intelligent data ocean management. This study developed a new 15Vs model empirically to examine distributed big image data processing based on a modern BID connectivity approach. The model accurately extracts textured features from visible data images and overcomes a unique set of key challenges. The model introduces a strategic analyzer, uses intelligent local agents, and recruits clever global bots to provide a suitable platform for generous support of bot-oriented BD processing. This instantly forms a hierarchical data level for better data acquisition and cleansing, safe privacy protection, suitable information diffusion, and speedy knowledge extraction. Our study presents a novel definition of BID to address the ultimate challenges of data management in a high-risk environment for further big image data modeling research needs. Big Image Data 15Vs Model Dark data Real-time Cyber-physical System Bot-oriented Processing Internet of Multimedia Things Intelligent Data Ocean Digital Twin 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4353622","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":300229619,"identity":"c14f7474-ed81-40d7-970f-8ae8d01fb76a","order_by":0,"name":"Aminollah Mahabadi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAfElEQVRIiWNgGAWjYFAC5gMHgCQPKVrYEkjWwmNAimog4J925uPhwhwGGfMGYrVI3M7dcHjmNgYemQNEWwPSwgvUIkG0DvnbOQ9I1GJwO4eBRC2Gt9MMgH6RIEGL3O3kx58Lt9nYE68FBJiBAUeSBrCWUTAKRsEoGAV4AADniyCKGRG4QAAAAABJRU5ErkJggg==","orcid":"","institution":"Institute for Research in Fundamental Sciences (IPM)","correspondingAuthor":true,"prefix":"","firstName":"Aminollah","middleName":"","lastName":"Mahabadi","suffix":""},{"id":300229621,"identity":"1e8c800e-2544-4510-b05a-e69547f20e80","order_by":1,"name":"Faezeh Abdolazimi","email":"","orcid":"","institution":"Shahed University","correspondingAuthor":false,"prefix":"","firstName":"Faezeh","middleName":"","lastName":"Abdolazimi","suffix":""}],"badges":[],"createdAt":"2024-05-01 10:02:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4353622/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4353622/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72160345,"identity":"b368789d-d58a-4133-97b6-2b3bdaf4f500","added_by":"auto","created_at":"2024-12-23 09:32:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5029724,"visible":true,"origin":"","legend":"","description":"","filename":"BIDMnauscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4353622/v1_covered_e82c703c-dfff-4cca-86c7-ec9fb172ee92.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things","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":"Big Image Data 15Vs Model, Dark data, Real-time Cyber-physical System, Bot-oriented Processing, Internet of Multimedia Things, Intelligent Data Ocean, Digital Twin","lastPublishedDoi":"10.21203/rs.3.rs-4353622/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4353622/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The classification of large image data using traditional methods has been based on changes in image gray features, extraction of edge and contour feature information, or conversion between image coordinate sets. 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