research-article

Understanding the bigger picture: batch-free exploration of streaming sequential patterns with accurate prediction

Authors:

Marwan Hassani,

Daniel Töws,

Thomas SeidlAuthors Info & Claims

SAC '17: Proceedings of the Symposium on Applied Computing

Pages 866 - 869

https://doi.org/10.1145/3019612.3019867

Published: 03 April 2017 Publication History

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Abstract

Finding sequential patterns in data streams has been an attractive research topic recently. Available approaches are able to bound the error of found patterns by using a static PrefixSpan approach. This usage forced a batch-based method to divide the stream into manageable chunks. However, discovering sequential patterns within batches of a stream encounters additional errors when compared to the continuous, non-batch way. First, a lot of patterns contain items from two consecutive batches and thus will be lost when each batch is processed individually. Second, some patterns may not be frequent in one batch, and thus will be pruned, even though they will appear frequently when considering multiple batches. In this paper, we present the BFSPMiner, a Batch-Free Sequential Pattern Miner algorithm that accurately explores patterns in streaming data. The proposed algorithm can efficiently find useful frequent patterns that are otherwise lost when applying batch-based approaches. In addition to addressing the above-mentioned issues, we show through extensive evaluations over multiple real-world datasets the high predictability of found patterns when compared with those generated from state-of-the-art batch-based algorithms.

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Cited By

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Bolta VHassani M(2023)Using Human Mobility Patterns to Forecast Outliers in Citizen Complaints Data2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386331(5166-5175)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386331
Han MDing J(2019)Efficient methods to set decay factor of time decay model over data streamsJournal of Intelligent & Fuzzy Systems10.3233/JIFS-181654(1-14)Online publication date: 9-May-2019
https://doi.org/10.3233/JIFS-181654
Hassani Mvan Zelst Svan der Aalst W(2019)On the application of sequential pattern mining primitives to process discovery: Overview, outlook and opportunity identificationWIREs Data Mining and Knowledge Discovery10.1002/widm.13159:6Online publication date: 10-May-2019
https://doi.org/10.1002/widm.1315
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Index Terms

Understanding the bigger picture: batch-free exploration of streaming sequential patterns with accurate prediction
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Data stream mining

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Published In

SAC '17: Proceedings of the Symposium on Applied Computing

April 2017

2004 pages

ISBN:9781450344869

DOI:10.1145/3019612

Conference Chair:
Sung Y. Shin
South Dakota State University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Maria Lencastre
University of Pernambuco, Brazil

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

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Published: 03 April 2017

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April 3 - 7, 2017

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Bolta VHassani M(2023)Using Human Mobility Patterns to Forecast Outliers in Citizen Complaints Data2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386331(5166-5175)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386331
Han MDing J(2019)Efficient methods to set decay factor of time decay model over data streamsJournal of Intelligent & Fuzzy Systems10.3233/JIFS-181654(1-14)Online publication date: 9-May-2019
https://doi.org/10.3233/JIFS-181654
Hassani Mvan Zelst Svan der Aalst W(2019)On the application of sequential pattern mining primitives to process discovery: Overview, outlook and opportunity identificationWIREs Data Mining and Knowledge Discovery10.1002/widm.13159:6Online publication date: 10-May-2019
https://doi.org/10.1002/widm.1315
Hassani MTöws DCuzzocrea ASeidl T(2017)BFSPMiner: an effective and efficient batch-free algorithm for mining sequential patterns over data streamsInternational Journal of Data Science and Analytics10.1007/s41060-017-0084-88:3(223-239)Online publication date: 26-Dec-2017
https://doi.org/10.1007/s41060-017-0084-8

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