GIDS: Efficient Grayscale Image-based Exemplar Spatial Dataset Search Processing

GIDS: Efficient Grayscale Image-based Exemplar Spatial Dataset Search Processing | 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 GIDS: Efficient Grayscale Image-based Exemplar Spatial Dataset Search Processing Mingyue Zhang, Hua Dai, Hao Zhou, Jie Sun, Pengyue Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8803849/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract In the data-driven era, dataset search has become a critical task in data science and engineering. Traditional spatial dataset search methods primarily rely on keyword or range queries, which are inadequate for capturing the user’s intent expressed through exemplar datasets. To address this limitation, this paper explores the problem of exemplar spatial dataset search, using exemplar datasets as input. A novel grayscale image-based similarity model is proposed, which maps the spatial distribution of datasets into grayscale images to capture detailed distribution features. Based on this model, a baseline search scheme, GIDS, is introduced. To further enhance search efficiency, an optimized search scheme, GIDS+, is presented, incorporating three key optimization strategies: a Morton code-based approach to accelerate similarity calculations and an \((\omega)\) -MSDtree-based and an upper-bound-based approach to enable efficient pruning during candidate filtering. Experiments conducted on three real-world spatial data repositories show that the proposed methods outperform existing approaches in terms of search efficiency, offering a new solution for spatial dataset search. Dataset Discovery Exemplar Dataset Search Grayscale Image Search Index Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Mar, 2026 Reviews received at journal 17 Mar, 2026 Reviews received at journal 13 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviews received at journal 18 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers invited by journal 16 Feb, 2026 Editor assigned by journal 16 Feb, 2026 Submission checks completed at journal 16 Feb, 2026 First submitted to journal 06 Feb, 2026 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-8803849","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593446526,"identity":"cf5e42ae-c9b4-4c15-8a69-c665d0bed288","order_by":0,"name":"Mingyue Zhang","email":"","orcid":"","institution":"Nanjing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Mingyue","middleName":"","lastName":"Zhang","suffix":""},{"id":593446528,"identity":"0dbfa6e8-ac94-4937-83ab-ce41cc640785","order_by":1,"name":"Hua Dai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDCCA4xtQNKGgbEBzGUmWksaSAtIF1FaGNiA5GEQk0gtfMcPtz3mqTmfxzwj9/gDhgrrxAb2swfwapE8k9huzHPsdjHjjLzEBoYz6YkNPHkJeLUYHEhsk+Zhu53YOCPHsIGx7XBigwSPAX4t5x8Ctfw7B9XyjxgtN4C28LYdgGppIEKL5I2HbZJz+5ITG3veJc5IOJZu3MaTg18L3/n0ZxJvvtklbmzPPfDhQ421bD/7Gfxa4MCwgYeBIQHIYCNOPRDIM/AQrXYUjIJRMApGGAAAXGlLoaRW/TwAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing University of Posts and Telecommunications","correspondingAuthor":true,"prefix":"","firstName":"Hua","middleName":"","lastName":"Dai","suffix":""},{"id":593446530,"identity":"c5fe82bf-8ca1-4de9-ad1f-b1133d590b6d","order_by":2,"name":"Hao Zhou","email":"","orcid":"","institution":"Nanjing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Zhou","suffix":""},{"id":593446531,"identity":"039e87e2-b17b-4708-8762-3221b51991c8","order_by":3,"name":"Jie Sun","email":"","orcid":"","institution":"Nanjing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Sun","suffix":""},{"id":593446532,"identity":"6b78a7ed-e66a-4d87-89e0-47dfb7cec3c4","order_by":4,"name":"Pengyue Li","email":"","orcid":"","institution":"Nanjing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Pengyue","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-02-06 07:24:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8803849/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8803849/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103505252,"identity":"bee41a30-d9a6-4e23-9cd5-e53f6dbacacc","added_by":"auto","created_at":"2026-02-26 13:28:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":782499,"visible":true,"origin":"","legend":"","description":"","filename":"JIISGIDSEfficientGrayscaleImagebasedExemplarSpatialDatasetSearchProcessing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8803849/v1_covered_91371cad-69ea-475f-be83-61dfdc9ac3dc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"GIDS: Efficient Grayscale Image-based Exemplar Spatial Dataset Search\nProcessing","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-intelligent-information-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jiis","sideBox":"Learn more about [Journal of Intelligent Information Systems](http://link.springer.com/journal/10844)","snPcode":"10844","submissionUrl":"https://submission.nature.com/new-submission/10844/3","title":"Journal of Intelligent Information Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Dataset Discovery, Exemplar Dataset Search, Grayscale Image, Search Index","lastPublishedDoi":"10.21203/rs.3.rs-8803849/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8803849/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the data-driven era, dataset search has become a critical task in data science and engineering. Traditional spatial dataset search methods primarily rely on keyword or range queries, which are inadequate for capturing the user\u0026rsquo;s intent expressed through exemplar datasets. To address this limitation, this paper explores the problem of exemplar spatial dataset search, using exemplar datasets as input. A novel grayscale image-based similarity model is proposed, which maps the spatial distribution of datasets into grayscale images to capture detailed distribution features. Based on this model, a baseline search scheme, GIDS, is introduced. 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