Liu, T., Li, H., Lu, H. et al. (2 more authors) (2024) Contact tracing over uncertain indoor positioning data (extended abstract). In: 2024 IEEE 40th International Conference on Data Engineering (ICDE). 40th IEEE International Conference on Data Engineering, 13-17 May 2024, Utrecht, Netherlands. Institute of Electrical and Electronics Engineers (IEEE) , pp. 5711-5712. ISBN 979-8-3503-1716-9
Abstract
Pandemics like COVID-19 often cause dramatic losses of human lives and societal impacts, urging efficient and effective contact tracing, especially in indoor venues where the risk of infection is higher. In this work, we formulate a novel query called Indoor Contact Query (ICQ) over raw, uncertain indoor positioning data that digitalizes people’s indoor mobility. Given a query object o, e.g., a virus-carrying person, an ICQ analyzes uncertain indoor positioning data to find objects that most likely had close contact with o for a long period of time. To process ICQ, we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary for ICQ. Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and multiple acceleration strategies. We conduct extensive experiments on synthetic and real datasets, which verify the efficiency and effectiveness of our proposals.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Author(s). Except as otherwise noted, this author-accepted version of a paper published in International Conference on Data Engineering is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | COVID-19; Pandemics; Query processing; Contact tracing; Data engineering; Data models; Proposals |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Feb 2024 12:19 |
Last Modified: | 09 Aug 2024 10:05 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/ICDE60146.2024.00487 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:209527 |