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Toward Mining Stop-by Behaviors in Indoor Space

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Kun-Ta ChuangAuthors Info & Claims

ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 3, Issue 2

Article No.: 7, Pages 1 - 38

https://doi.org/10.1145/3106736

Published: 29 August 2017 Publication History

Abstract

In this article, we explore a new mining paradigm, called Indoor Stop-by Patterns (ISP), to discover user stop-by behavior in mall-like indoor environments. The discovery of ISPs enables new marketing collaborations, such as a joint coupon promotion, among stores in indoor spaces (e.g., shopping malls). Moreover, it can also help in eliminating the overcrowding situation. To pursue better practicability, we consider the cost-effective wireless sensor-based environment and conduct the analysis of indoor stop-by behaviors on real data. However, it is a highly challenging issue, in indoor environments, to retrieve frequent ISPs, especially when the issue of user privacy is highlighted nowadays. The mining of ISPs will face a critical challenge from spatial uncertainty. Previous work on mining indoor movement patterns usually relies on precise spatio-temporal information by a specific deployment of positioning devices, which cannot be directly applied. In this article, the proposed Probabilistic Top-k Indoor Stop-by Patterns Discovery (PTkISP) framework incorporates the probabilistic model to identify top-k ISPs over uncertain data collected from sensing logs. Moreover, we develop an uncertain model and devise an Index 1-itemset (IIS) algorithm to enhance the accuracy and efficiency. Our experimental studies show that the proposed PTkISP framework can efficiently discover high-quality ISPs and can provide insightful observations for marketing collaborations.

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https://doi.org/10.1109/TSC.2024.3463419
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
Li HTang BLu HCheema MJensen CIves ZBonifati AEl Abbadi A(2022)Spatial Data Quality in the IoT Era: Management and ExploitationProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3522568(2474-2482)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3522568
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Index Terms

Toward Mining Stop-by Behaviors in Indoor Space
1. Theory of computation
  1. Theory and algorithms for application domains

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Published In

cover image ACM Transactions on Spatial Algorithms and Systems

ACM Transactions on Spatial Algorithms and Systems Volume 3, Issue 2

SIGSPATIAL Paper and Regular Papers

June 2017

132 pages

ISSN:2374-0353

EISSN:2374-0361

DOI:10.1145/3129284

Editor:
Hanan Samet
University of Maryland, College Park

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Copyright © 2017 ACM.

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Publication History

Published: 29 August 2017

Accepted: 01 June 2017

Revised: 01 March 2017

Received: 01 September 2016

Published in TSAS Volume 3, Issue 2

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Cited By

Imamura HSakai KSun MKu WWu J(2024)Modeling Real-Time Task Assignment for Mobile Crowdsourcing in Opportunistic NetworksIEEE Transactions on Services Computing10.1109/TSC.2024.3463419(1-14)Online publication date: 2024
https://doi.org/10.1109/TSC.2024.3463419
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
Li HTang BLu HCheema MJensen CIves ZBonifati AEl Abbadi A(2022)Spatial Data Quality in the IoT Era: Management and ExploitationProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3522568(2474-2482)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3522568
Li HLu HJensen CTang BCheema M(2022)Spatial Data Quality in the Internet of Things: Management, Exploitation, and ProspectsACM Computing Surveys10.1145/349833855:3(1-41)Online publication date: 3-Feb-2022
https://dl.acm.org/doi/10.1145/3498338
Liu ZShi WYu YChen PYu Chen B(2022)A LSTM-based approach for modelling the movement uncertainty of indoor trajectories with mobile sensing dataInternational Journal of Applied Earth Observation and Geoinformation10.1016/j.jag.2022.102758108(102758)Online publication date: Apr-2022
https://doi.org/10.1016/j.jag.2022.102758
Monica SBergenti F(2020)An Algorithm for Accurate and Robust Indoor Localization Based on Nonlinear ProgrammingElectronics10.3390/electronics90100659:1(65)Online publication date: 1-Jan-2020
https://doi.org/10.3390/electronics9010065
Monica SBergenti F(2020)Interval arithmetic to support effective indoor positioning of software agentsIntelligenza Artificiale10.3233/IA-19004214:1(59-73)Online publication date: 17-Sep-2020
https://doi.org/10.3233/IA-190042
Li HLu HCheema MShou LChen G(2020)Indoor Mobility Semantics Annotation Using Coupled Conditional Markov Networks2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00128(1441-1452)Online publication date: Apr-2020
https://doi.org/10.1109/ICDE48307.2020.00128

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