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SLIM : Scalable Linkage of Mobility Data
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Basik, Fuat, Ferhatosmanoglu, Hakan and Gedik, Bugra (2020) SLIM : Scalable Linkage of Mobility Data. In: ACM SIGMOD/PODS International Conference on Management of Data, Portland, OR, USA, 14- 19 Jun 2020. Published in: SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data pp. 118-1196. ISBN 9781450367356. doi:10.1145/3318464.3389761
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WRAP-scalable-linkage-mobility-data-Ferhatosmanoglu-2020.pdf - Accepted Version - Requires a PDF viewer. Download (1514Kb) | Preview |
Official URL: https://doi.org/10.1145/3318464.3389761
Abstract
We present a scalable solution to link entities across mobility datasets using their spatio-temporal information. This is a fundamental problem in many applications such as linking user identities for security, understanding privacy limitations of location based services, or producing a unified dataset from multiple sources for urban planning. Such integrated datasets are also essential for service providers to optimise their services and improve business intelligence. In this paper, we first propose a mobility based representation and similarity computation for entities. An efficient matching process is then developed to identify the final linked pairs, with an automated mechanism to decide when to stop the linkage. We scale the process with a locality-sensitive hashing (LSH) based approach that significantly reduces candidate pairs for matching. To realize the effectiveness and efficiency of our techniques in practice, we introduce an algorithm called SLIM. In the experimental evaluation, SLIM outperforms the two existing state-of-the-art approaches in terms of precision and recall. Moreover, the LSH-based approach brings two to four orders of magnitude speedup.
Item Type: | Conference Item (Paper) | ||||||
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Alternative Title: | |||||||
Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Data integration (Computer science) , Computer networks -- Scalability , Statistical matching | ||||||
Journal or Publication Title: | SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data | ||||||
Publisher: | ACM | ||||||
ISBN: | 9781450367356 | ||||||
Official Date: | 11 June 2020 | ||||||
Dates: |
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Page Range: | pp. 118-1196 | ||||||
DOI: | 10.1145/3318464.3389761 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Reuse Statement (publisher, data, author rights): | "© ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PUBLICATION, {VOL#, ISS#, (DATE)} http://doi.acm.org/10.1145/nnnnnn.nnnnnn" | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 20 April 2020 | ||||||
Date of first compliant Open Access: | 20 April 2020 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | ACM SIGMOD/PODS International Conference on Management of Data | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Portland, OR, USA | ||||||
Date(s) of Event: | 14- 19 Jun 2020 | ||||||
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