Fuzzy Utility Stream Pattern Analysis with Temporal Aspects on Damped Window Model

Fuzzy Utility Stream Pattern Analysis with Temporal Aspects on Damped Window Model | 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 Fuzzy Utility Stream Pattern Analysis with Temporal Aspects on Damped Window Model Unil Yun, Doyoung Kim, Seungwan Park, Seongbin Park, Hanju Kim, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7016344/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 Fuzzy logic provides a framework for modeling linguistic imprecision in real-world data. It has been widely applied to pattern analysis to enhance interpretability for supporting decision-making processes. High temporal fuzzy utility pattern analysis has garnered increasing attention for its ability to extract interpretable and representative patterns by integrating temporal characteristics, enabling more informed decisions in areas such as promotion planning or resource allocation. However, previous approaches in this domain operate under the assumption that all transactions hold equal significance, irrespective of their arrival times. This assumption is unrealistic, as real-world data evolve and recent transactions hold greater importance, especially for decisions that rely on timely trend responsiveness. To resolve this shortcoming, we propose an efficient approach for analyzing high temporal fuzzy utility patterns over data streams, grounded in the damped window technique. The proposed approach leverages a decaying factor to emphasize the significance of recent data. To achieve computational efficiency, the pattern expansion process employs list-based structures along with multiple pruning strategies. Comprehensive experiments indicate that the proposed method surpasses previous methods regarding runtime and scalability with competitive memory usage while exhibiting the significance of extracted results and showing practical availability through a case study. The analysis of extracted patterns highlights their relevance within a practical retail scenario, and experiments with varying decay factors corroborate the efficiency and adaptability of the proposed method for offering timely and interpretable insights in dynamic environments. Fuzzy utility stream temporal aspect stream pattern analysis damped window model 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-7016344","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512244688,"identity":"eb920e0d-c873-44a6-ad2d-836c638c8e9b","order_by":0,"name":"Unil Yun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYLCChAoGBsYGKEeCOC1nSNbC2IbEIajF4EbuwQcP59XJMbf3Hn7BUGPHIDn7ACEteckGidsOGzP2nEuzYDiWzCDNl0BIS46ZROK2A4mNM3LMDBjYDjDI8RB0WI75j8Q5dfUQLf+I02LGkNjAnMA4I8f4AWPbAQZpQlokz7wxlkg4dtiwsecMUG9fMo9kDwEtfMdzDD/+qKmTN2zvMf7w4ZudnMQZAloUDkAZhg0MbBIJDAyEnMXAIN8AYzAwMH8gqHwUjIJRMApGJAAAnHBBhlOCVEcAAAAASUVORK5CYII=","orcid":"","institution":"Sejong University","correspondingAuthor":true,"prefix":"","firstName":"Unil","middleName":"","lastName":"Yun","suffix":""},{"id":512244690,"identity":"1a594d9e-e368-4546-b13e-456e9da57d47","order_by":1,"name":"Doyoung Kim","email":"","orcid":"","institution":"Sejong University","correspondingAuthor":false,"prefix":"","firstName":"Doyoung","middleName":"","lastName":"Kim","suffix":""},{"id":512244691,"identity":"b294d237-a059-4dcb-8102-5afbcf5f7c3f","order_by":2,"name":"Seungwan Park","email":"","orcid":"","institution":"Sejong University","correspondingAuthor":false,"prefix":"","firstName":"Seungwan","middleName":"","lastName":"Park","suffix":""},{"id":512244692,"identity":"b1260f06-55e9-4cdf-8f81-736547b583ff","order_by":3,"name":"Seongbin Park","email":"","orcid":"","institution":"Sejong University","correspondingAuthor":false,"prefix":"","firstName":"Seongbin","middleName":"","lastName":"Park","suffix":""},{"id":512244694,"identity":"9f77ff0e-4ad2-47ee-86ac-37455f88508c","order_by":4,"name":"Hanju Kim","email":"","orcid":"","institution":"Sejong University","correspondingAuthor":false,"prefix":"","firstName":"Hanju","middleName":"","lastName":"Kim","suffix":""},{"id":512244696,"identity":"3d8adf78-fa57-4aa7-868a-aef867cf8ad2","order_by":5,"name":"Myungha Cho","email":"","orcid":"","institution":"Sejong University","correspondingAuthor":false,"prefix":"","firstName":"Myungha","middleName":"","lastName":"Cho","suffix":""},{"id":512244698,"identity":"2810c24d-5ea3-49b5-b549-87c5a2291ca4","order_by":6,"name":"Junyoung Park","email":"","orcid":"","institution":"Sejong University","correspondingAuthor":false,"prefix":"","firstName":"Junyoung","middleName":"","lastName":"Park","suffix":""}],"badges":[],"createdAt":"2025-07-01 05:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7016344/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7016344/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91303576,"identity":"b8c9caed-3d0f-477c-803e-bbc949478ef0","added_by":"auto","created_at":"2025-09-15 06:15:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2431314,"visible":true,"origin":"","legend":"","description":"","filename":"FODMD1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7016344/v1_covered_4fc44e33-ef2f-419d-8ac8-508d753cebe2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fuzzy Utility Stream Pattern Analysis with Temporal Aspects on Damped Window Model","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":"Fuzzy utility stream, temporal aspect, stream pattern analysis, damped window model","lastPublishedDoi":"10.21203/rs.3.rs-7016344/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7016344/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Fuzzy logic provides a framework for modeling linguistic imprecision in real-world data. It has been widely applied to pattern analysis to enhance interpretability for supporting decision-making processes. High temporal fuzzy utility pattern analysis has garnered increasing attention for its ability to extract interpretable and representative patterns by integrating temporal characteristics, enabling more informed decisions in areas such as promotion planning or resource allocation. However, previous approaches in this domain operate under the assumption that all transactions hold equal significance, irrespective of their arrival times. This assumption is unrealistic, as real-world data evolve and recent transactions hold greater importance, especially for decisions that rely on timely trend responsiveness. To resolve this shortcoming, we propose an efficient approach for analyzing high temporal fuzzy utility patterns over data streams, grounded in the damped window technique. The proposed approach leverages a decaying factor to emphasize the significance of recent data. 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