research-article

Z-Miner: An Efficient Method for Mining Frequent Arrangements of Event Intervals

Authors:

Panagiotis PapapetrouAuthors Info & Claims

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 524 - 534

https://doi.org/10.1145/3394486.3403095

Published: 20 August 2020 Publication History

Abstract

Mining frequent patterns of event intervals from a large collection of interval sequences is a problem that appears in several application domains. In this paper, we propose Z-Miner, a novel algorithm for solving this problem that addresses the deficiencies of existing competitors by employing two novel data structures: Z-Table, a hierarchical hash-based data structure for time-efficient candidate generation and support count, and Z-Arrangement, a data structure for efficient memory consumption. The proposed algorithm is able to handle patterns with repetitions of the same event label, allowing for gap and error tolerance constraints, as well as keeping track of the exact occurrences of the extracted frequent patterns. Our experimental evaluation on eight real-world and six synthetic datasets demonstrates the superiority of Z-Miner against four state-of-the-art competitors in terms of runtime efficiency and memory footprint.

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Mining frequent patterns of event intervals from a large collection of interval sequences is a problem that appears in several application domains. In this paper, we propose Z-Miner, a novel algorithm for solving this problem that addresses the deficiencies of existing competitors by employing two novel data structures: Z-Table, a hierarchical hash-based data structure for time-efficient candidate generation and support count, and Z-Arrangement, a data structure for efficient memory consumption. The proposed algorithm is able to handle patterns with repetitions of the same event label, allowing for gap and error tolerance constraints, as well as keeping track of the exact occurrences of the extracted frequent patterns. Our experimental evaluation on eight real-world and six synthetic datasets demonstrates the superiority of Z-Miner against four state-of-the-art competitors in terms of runtime efficiency and memory footprint.

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Cited By

WU YZHOU HPENG LYANG TWANG MWANG G(2024)Unit coordination knowledge enhanced autonomous decision-making approach of heterogeneous UAV formationChinese Journal of Aeronautics10.1016/j.cja.2024.08.037Online publication date: Aug-2024
https://doi.org/10.1016/j.cja.2024.08.037
Wang GPeng LWang MWang G(2024)Effectiveness evaluation of shipboard manned/unmanned aerial vehicle synergy based on CFP-minerAerospace Systems10.1007/s42401-024-00310-4Online publication date: 11-Jul-2024
https://doi.org/10.1007/s42401-024-00310-4
Shknevsky AShahar YMoskovitch R(2023)The Semantic Adjacency Criterion in Time Intervals MiningBig Data and Cognitive Computing10.3390/bdcc70401737:4(173)Online publication date: 9-Nov-2023
https://doi.org/10.3390/bdcc7040173
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Index Terms

Z-Miner: An Efficient Method for Mining Frequent Arrangements of Event Intervals
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Temporal reasoning
  2. Machine learning
    1. Machine learning approaches

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cover image ACM Conferences

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

August 2020

3664 pages

ISBN:9781450379984

DOI:10.1145/3394486

General Chairs:
Rajesh Gupta
UC San Diego, USA
,
Yan Liu
USC, USA
,
Program Chairs:
Mohak Shah
LG Electronics, USA
,
Suju Rajan
Linkedin, USA
,
Publications Chairs:
Jiliang Tang
Michigan State, USA
,
B. Aditya Prakash
Georgia Tech, USA

Copyright © 2020 ACM.

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Published: 20 August 2020

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Swedish Research Council Starting Grant
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KDD '20

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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

July 6 - 10, 2020

CA, Virtual Event, USA

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

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Cited By

WU YZHOU HPENG LYANG TWANG MWANG G(2024)Unit coordination knowledge enhanced autonomous decision-making approach of heterogeneous UAV formationChinese Journal of Aeronautics10.1016/j.cja.2024.08.037Online publication date: Aug-2024
https://doi.org/10.1016/j.cja.2024.08.037
Wang GPeng LWang MWang G(2024)Effectiveness evaluation of shipboard manned/unmanned aerial vehicle synergy based on CFP-minerAerospace Systems10.1007/s42401-024-00310-4Online publication date: 11-Jul-2024
https://doi.org/10.1007/s42401-024-00310-4
Shknevsky AShahar YMoskovitch R(2023)The Semantic Adjacency Criterion in Time Intervals MiningBig Data and Cognitive Computing10.3390/bdcc70401737:4(173)Online publication date: 9-Nov-2023
https://doi.org/10.3390/bdcc7040173
Dong XWang XPeng LWang MWang G(2023)An Efficient Task Synthesis Method Based on Subspace Differential Patterns for Arrangements of Event Intervals Mining in the Avionics Cloud System ArchitectureAerospace10.3390/aerospace1003024910:3(249)Online publication date: 5-Mar-2023
https://doi.org/10.3390/aerospace10030249
Ho VHo NPedersen T(2023)Mining Seasonal Temporal Patterns in Time Series2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00174(2249-2261)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00174
Lee ZLindgren TPapapetrou P(2023)Z-Time: efficient and effective interpretable multivariate time series classificationData Mining and Knowledge Discovery10.1007/s10618-023-00969-x38:1(206-236)Online publication date: 5-Sep-2023
https://doi.org/10.1007/s10618-023-00969-x
Harel OMoskovitch R(2023)TIRPClo: efficient and complete mining of time intervals-related patternsData Mining and Knowledge Discovery10.1007/s10618-023-00944-637:5(1806-1857)Online publication date: 30-Jun-2023
https://doi.org/10.1007/s10618-023-00944-6
Itzhak NJaroszewicz SMoskovitch R(2023)Continuous prediction of a time intervals-related pattern’s completionKnowledge and Information Systems10.1007/s10115-023-01910-w65:11(4797-4846)Online publication date: 24-Jun-2023
https://doi.org/10.1007/s10115-023-01910-w
Fournier-Viger PLi YNawaz MHe Y(2022)FastTIRP: Efficient Discovery of Time-Interval Related PatternsBig Data Analytics10.1007/978-3-031-24094-2_13(185-199)Online publication date: 19-Dec-2022
https://dl.acm.org/doi/10.1007/978-3-031-24094-2_13
Ho VHo NPedersen T(2021)Efficient temporal pattern mining in big time series using mutual informationProceedings of the VLDB Endowment10.14778/3494124.349414715:3(673-685)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.14778/3494124.3494147
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