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Toward Translating Raw Indoor Positioning Data into Mobility Semantics

Authors:

Lidan ShouAuthors Info & Claims

ACM Transactions on Data Science, Volume 1, Issue 4

Article No.: 26, Pages 1 - 37

https://doi.org/10.1145/3385190

Published: 25 November 2020 Publication History

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Abstract

Indoor mobility analyses are increasingly interesting due to the rapid growth of raw indoor positioning data obtained from IoT infrastructure. However, high-level analyses are still in urgent need of a concise but semantics-oriented representation of the mobility implied by the raw data. This work studies the problem of translating raw indoor positioning data into mobility semantics that describe a moving object’s mobility event (What) someplace (Where) at some time (When). The problem is non-trivial mainly because of the inherent errors in the uncertain, discrete raw data. We propose a three-layer framework to tackle the problem. In the cleaning layer, we design a cleaning method that eliminates positioning data errors by considering indoor mobility constraints. In the annotation layer, we propose a split-and-match approach to annotate mobility semantics on the cleaned data. The approach first employs a density-based splitting method to divide positioning sequences into split snippets according to underlying mobility events, followed by a semantic matching method that makes proper annotations for split snippets. In the complementing layer, we devise an inference method that makes use of the indoor topology and the mobility semantics already obtained to recover the missing mobility semantics. The extensive experiments demonstrate that our solution is efficient and effective on both real and synthetic data. For typical queries, our solution’s resultant mobility semantics lead to more precise answers but incur less execution time than alternatives.

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Index Terms

Toward Translating Raw Indoor Positioning Data into Mobility Semantics
1. Information systems
  1. Information systems applications

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cover image ACM/IMS Transactions on Data Science

ACM/IMS Transactions on Data Science Volume 1, Issue 4

Special Issue on Retrieving and Learning from IoT Data and Regular Papers

November 2020

148 pages

ISSN:2691-1922

DOI:10.1145/3439709

Editor:
Beng Chin Ooi
National University of Singapore, Singapore

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Copyright © 2020 ACM.

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Publication History

Published: 25 November 2020

Online AM: 07 May 2020

Accepted: 01 January 2020

Revised: 01 November 2019

Received: 01 August 2019

Published in TDS Volume 1, Issue 4

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National Science Foundation of China
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Natural Science Foundation of Zhejiang Province of China

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Akbari FSartipi KArcher N(2023)Synthetic Behavior Sequence Generation Using Generative Adversarial NetworksACM Transactions on Computing for Healthcare10.1145/35639504:1(1-23)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3563950
Saggese FChiariotti FKansanen KPopovski P(2023)Efficient URLLC with a Reconfigurable Intelligent Surface and Imperfect Device TrackingICC 2023 - IEEE International Conference on Communications10.1109/ICC45041.2023.10278771(2722-2728)Online publication date: 28-May-2023
https://doi.org/10.1109/ICC45041.2023.10278771
Li HLiu TChan HLu H(2022)Spatial data analysis for intelligent buildings: Awareness of context and data uncertaintyFrontiers in Big Data10.3389/fdata.2022.10491985Online publication date: 7-Nov-2022
https://doi.org/10.3389/fdata.2022.1049198
Chan HLi HLi XLu H(2022)Continuous social distance monitoring in indoor spaceProceedings of the VLDB Endowment10.14778/3523210.352321715:7(1390-1402)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.14778/3523210.3523217
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