Durable Queries over Non-synchronized Temporal Data

Durable Queries over Non-synchronized Temporal 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Article Durable Queries over Non-synchronized Temporal Data Yanqi Xie, Wei Weng, Jianmin Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2120736/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Temporal data are ubiquitous nowadays and efficient management of temporal data is of key importance. A temporal data typically describes the evolution of an object over time. One of the most useful queries over temporal data are the durable top- k queries. Given a time window, a durable top- k query finds the objects that are frequently among the best. Existing solutions to durable top- k queries assume that all temporal data are sampled at the same time points (i.e., at any time, there is a corresponding observed value for every temporal data). However, in many practical applications, temporal data are collected from multiple data sources with different sampling rates. In this light, we investigate the efficient processing of durable top- k queries over temporal data with different sampling rates.We propose an efficient sweep line algorithm to process durable top- k queries over non-synchronized temporal data. We conduct extensive experiments on two real datasets to test the performance of our proposed method. The results show that our methods outperforms the baseline solutions by a large margin. Durable Top-k Query Processing Non-synchronized Temporal Data Sweep Line Algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 15 Oct, 2022 Reviews received at journal 15 Oct, 2022 Reviewers agreed at journal 07 Oct, 2022 Reviewers invited by journal 06 Oct, 2022 Editor assigned by journal 06 Oct, 2022 Submission checks completed at journal 06 Oct, 2022 First submitted to journal 30 Sep, 2022 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. 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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-2120736","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":142305676,"identity":"6198a327-7652-497e-b7db-ff12340306c2","order_by":0,"name":"Yanqi Xie","email":"","orcid":"","institution":"Xiamen University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanqi","middleName":"","lastName":"Xie","suffix":""},{"id":142305678,"identity":"ffddd3c9-a859-46b7-addf-1ac174237927","order_by":1,"name":"Wei Weng","email":"","orcid":"","institution":"Xiamen University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Weng","suffix":""},{"id":142305680,"identity":"44417a63-1b4a-4fd2-87e7-f83ee43ad460","order_by":2,"name":"Jianmin Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBACxmYQacAgwwbh2zCwEauFB6IyIY2wFhjggVAJhwkrZW5nfvboRsEdHj4GHtPNvD/O2/NJNz9g+FGxDY/D2MyNcwyeAR3GlnabJ+F2YpvMMQPGnjO38fnFTDrH4DBQC/MxkJYENokEA2bGNnxa2L9BtQCV8SScs2eTSP9AQAsPii0HGNskcgjZwlMG1cKWdnNOWnIiUEvBQXx+Mew/vk06589hOfkGHrMbb2zs7OVnpG988KMCj5YGGEv+AUL0AE71IIX4JEfBKBgFo2AUgAEA4HJKJAFEJT4AAAAASUVORK5CYII=","orcid":"","institution":"Xiamen University of Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jianmin","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2022-09-30 13:59:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2120736/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2120736/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":27581692,"identity":"8313239f-cac0-4ef0-bf44-04495fd291aa","added_by":"auto","created_at":"2022-10-10 20:56:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":452750,"visible":true,"origin":"","legend":"","description":"","filename":"WWWJSep25R2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2120736/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Durable Queries over Non-synchronized Temporal Data","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-2120736/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"world-wide-web","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wwwj","sideBox":"Learn more about [World Wide Web](http://link.springer.com/journal/11280)","snPcode":"11280","submissionUrl":"https://submission.nature.com/new-submission/11280/3","title":"World Wide Web","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Durable Top-k, Query Processing, Non-synchronized Temporal, Data, Sweep Line Algorithm","lastPublishedDoi":"10.21203/rs.3.rs-2120736/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2120736/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTemporal data are ubiquitous nowadays and efficient management of temporal data is of key importance. A temporal data typically describes the evolution of an object over time. One of the most useful queries over temporal data are the durable top-\u003cem\u003ek\u003c/em\u003e queries. Given a time window, a durable top-\u003cem\u003ek\u003c/em\u003e query finds the objects that are frequently among the best. Existing solutions to durable top-\u003cem\u003ek\u003c/em\u003e queries assume that all temporal data are sampled at the same time points (i.e., at any time, there is a corresponding observed value for every temporal data). However, in many practical applications, temporal data are collected from multiple data sources with different sampling rates. In this light, we investigate the efficient processing of durable top-\u003cem\u003ek\u003c/em\u003e queries over temporal data with different sampling rates.We propose an efficient sweep line algorithm to process durable top-\u003cem\u003ek\u003c/em\u003e queries over non-synchronized temporal data. 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