research-article

Mining event periodicity from incomplete observations

Authors:

Jiawei HanAuthors Info & Claims

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 444 - 452

https://doi.org/10.1145/2339530.2339604

Published: 12 August 2012 Publication History

Abstract

Advanced technology in GPS and sensors enables us to track physical events, such as human movements and facility usage. Periodicity analysis from the recorded data is an important data mining task which provides useful insights into the physical events and enables us to report outliers and predict future behaviors. To mine periodicity in an event, we have to face real-world challenges of inherently complicated periodic behaviors and imperfect data collection problem. Specifically, the hidden temporal periodic behaviors could be oscillating and noisy, and the observations of the event could be incomplete.

In this paper, we propose a novel probabilistic measure for periodicity and design a practical method to detect periods. Our method has thoroughly considered the uncertainties and noises in periodic behaviors and is provably robust to incomplete observations. Comprehensive experiments on both synthetic and real datasets demonstrate the effectiveness of our method.

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Cited By

Flüchter MLindner SOsswald LArnaud JMenth M(2024)Autonomous integration of TSN-unaware applications with QoS requirements in TSN networksComputer Communications10.1016/j.comcom.2024.04.021222(118-129)Online publication date: Jun-2024
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Zhang YDong HNottingham ABuchanan MBrown DSun Y(2023)Global Analysis with Aggregation-based Beaconing Detection across Large Campus NetworksProceedings of the 39th Annual Computer Security Applications Conference10.1145/3627106.3627126(565-579)Online publication date: 4-Dec-2023
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Index Terms

Mining event periodicity from incomplete observations
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2012

1616 pages

ISBN:9781450314626

DOI:10.1145/2339530

General Chair:
Qiang Yang
Hong Kong University of Science and Technology
,
Program Chairs:
Deepak Agarwal
LinkedIn
,
Jian Pei
Simon Fraser University

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 August 2012

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KDD '12

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KDD '12: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 16, 2012

Beijing, China

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Flüchter MLindner SOsswald LArnaud JMenth M(2024)Autonomous integration of TSN-unaware applications with QoS requirements in TSN networksComputer Communications10.1016/j.comcom.2024.04.021222(118-129)Online publication date: Jun-2024
https://doi.org/10.1016/j.comcom.2024.04.021
Zhang YDong HNottingham ABuchanan MBrown DSun Y(2023)Global Analysis with Aggregation-based Beaconing Detection across Large Campus NetworksProceedings of the 39th Annual Computer Security Applications Conference10.1145/3627106.3627126(565-579)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3627106.3627126
Wen YYang HPeng W(2022)Mining Willing-to-Pay Behavior Patterns from Payment DatasetsACM Transactions on Intelligent Systems and Technology10.1145/348584813:1(1-19)Online publication date: 6-Feb-2022
https://dl.acm.org/doi/10.1145/3485848
Jin FHua WZhou TXu JFrancia MOrlowska MZhou X(2022)Trajectory-Based Spatiotemporal Entity LinkingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.303663334:9(4499-4513)Online publication date: 1-Sep-2022
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Ben Said AErradi A(2022)Spatiotemporal Tensor Completion for Improved Urban Traffic ImputationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.306299923:7(6836-6849)Online publication date: Jul-2022
https://doi.org/10.1109/TITS.2021.3062999
Wen QHe KSun LZhang YKe MXu HLi GLi ZIdreos SSrivastava D(2021)RobustPeriod: Robust Time-Frequency Mining for Multiple Periodicity DetectionProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452779(2328-2337)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3452779
Zhang CHan J(2021)Data Mining and Knowledge DiscoveryUrban Informatics10.1007/978-981-15-8983-6_42(797-814)Online publication date: 7-Apr-2021
https://doi.org/10.1007/978-981-15-8983-6_42
Puech TBoussard MD’Amato AMillerand G(2020)A Fully Automated Periodicity Detection in Time SeriesAdvanced Analytics and Learning on Temporal Data10.1007/978-3-030-39098-3_4(43-54)Online publication date: 23-Jan-2020
https://doi.org/10.1007/978-3-030-39098-3_4
Tsubouchi KSaito TShimosaka M(2019)Context-based Markov Model toward Spatio-Temporal Prediction with Realistic DatasetProceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility10.1145/3356995.3364534(24-32)Online publication date: 5-Nov-2019
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Jin FHua WXu JZhou X(2019)Moving Object Linking Based on Historical Trace2019 IEEE 35th International Conference on Data Engineering (ICDE)10.1109/ICDE.2019.00098(1058-1069)Online publication date: Apr-2019
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