Article

A fast algorithm for finding frequent episodes in event streams

Authors:

Srivatsan Laxman,

P. S. Sastry,

K. P. UnnikrishnanAuthors Info & Claims

KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 410 - 419

https://doi.org/10.1145/1281192.1281238

Published: 12 August 2007 Publication History

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Abstract

Frequent episode discovery is a popular framework for mining data available as a long sequence of events. An episode is essentially a short ordered sequence of event types and the frequency of an episode is some suitable measure of how often the episode occurs in the data sequence. Recently,we proposed a new frequency measure for episodes based on the notion of non-overlapped occurrences of episodes in the event sequence, and showed that, such a definition, in addition to yielding computationally efficient algorithms, has some important theoretical properties in connecting frequent episode discovery with HMM learning. This paper presents some new algorithms for frequent episode discovery under this non-overlapped occurrences-based frequency definition. The algorithms presented here are better (by a factor of N, where N denotes the size of episodes being discovered) in terms of both time and space complexities when compared to existing methods for frequent episode discovery. We show through some simulation experiments, that our algorithms are very efficient. The new algorithms presented here have arguably the least possible orders of spaceand time complexities for the task of frequent episode discovery.

References

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Cited By

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Gandreti SA. IP. S. S(2024)Breadth-First Search Approach for Mining Serial Episodes with Simultaneous EventsProceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632445(36-44)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1145/3632410.3632445
Gan WLin JChao HYu P(2023)Discovering High Utility Episodes in SequencesIEEE Transactions on Artificial Intelligence10.1109/TAI.2022.32239654:3(473-486)Online publication date: Jun-2023
https://doi.org/10.1109/TAI.2022.3223965
Ouarem ONouioua FFournier-Viger P(2023)Discovering frequent parallel episodes in complex event sequences by counting distinct occurrencesApplied Intelligence10.1007/s10489-023-05187-y54:1(701-721)Online publication date: 18-Dec-2023
https://doi.org/10.1007/s10489-023-05187-y
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Index Terms

A fast algorithm for finding frequent episodes in event streams
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2007

1080 pages

ISBN:9781595936097

DOI:10.1145/1281192

General Chair:
Pavel Berkhin
Yahoo!, USA
,
Program Chairs:
Rich Caruana
Cornell University, USA
,
Xindong Wu
University of Vermont, USA

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2007

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KDD07

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KDD07: The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 15, 2007

California, San Jose, USA

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KDD '07 Paper Acceptance Rate 111 of 573 submissions, 19%;

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

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Cited By

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Gandreti SA. IP. S. S(2024)Breadth-First Search Approach for Mining Serial Episodes with Simultaneous EventsProceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632445(36-44)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1145/3632410.3632445
Gan WLin JChao HYu P(2023)Discovering High Utility Episodes in SequencesIEEE Transactions on Artificial Intelligence10.1109/TAI.2022.32239654:3(473-486)Online publication date: Jun-2023
https://doi.org/10.1109/TAI.2022.3223965
Ouarem ONouioua FFournier-Viger P(2023)Discovering frequent parallel episodes in complex event sequences by counting distinct occurrencesApplied Intelligence10.1007/s10489-023-05187-y54:1(701-721)Online publication date: 18-Dec-2023
https://doi.org/10.1007/s10489-023-05187-y
Ouarem ONouioua FFournier‐Viger P(2023)A survey of episode miningWIREs Data Mining and Knowledge Discovery10.1002/widm.152414:2Online publication date: 28-Nov-2023
https://doi.org/10.1002/widm.1524
Qin JWang JLi QFang SLi XLei L(2022)Differentially private frequent episode mining over event streamsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.104681110:COnline publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.engappai.2022.104681
Feremans LCule BGoethals B(2022)PETSC: pattern-based embedding for time series classificationData Mining and Knowledge Discovery10.1007/s10618-022-00822-736:3(1015-1061)Online publication date: 24-Mar-2022
https://doi.org/10.1007/s10618-022-00822-7
Cüppers JKalofolias JVreeken J(2022)Omen: discovering sequential patterns with reliable prediction delaysKnowledge and Information Systems10.1007/s10115-022-01660-164:4(1013-1045)Online publication date: 5-Mar-2022
https://doi.org/10.1007/s10115-022-01660-1
Mehnaz SBertino E(2021)A Fine-grained Approach for Anomaly Detection in File System Accesses with Enhanced Temporal User ProfilesIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2019.2954507(1-1)Online publication date: 2021
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Ouarem ONouioua FFournier-Viger P(2021)Mining Episode Rules from Event Sequences Under Non-overlapping FrequencyAdvances and Trends in Artificial Intelligence. Artificial Intelligence Practices10.1007/978-3-030-79457-6_7(73-85)Online publication date: 19-Jul-2021
https://doi.org/10.1007/978-3-030-79457-6_7
Cuppers JVreeken J(2020)Just Wait for it... Mining Sequential Patterns with Reliable Prediction Delays2020 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM50108.2020.00017(82-91)Online publication date: Nov-2020
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