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FastTIRP: Efficient Discovery of Time-Interval Related Patterns

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Big Data Analytics (BDA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13773))

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Abstract

Finding frequent patterns in discrete sequences of symbols or events can be useful to understand data, support decision-making and make predictions. However, many studies on analyzing event sequence do not consider the duration of events, and thus the complex time relationships between them (e.g. an event may start at the same time as another event but end before). To find frequent sequential patterns in data where events have a start and end time, an emerging topic is time-interval related pattern (TIRP) mining. Several algorithms have been proposed for this task but efficiency remains a major issue due to the very large search space. To provide a more efficient algorithm for TIRP mining, this paper presents a novel algorithm called FastTIRP. It utilizes a novel Pair Support Pruning (PSP) optimization to reduce the search space. Experiments show that FastTIRP outperforms the state-of-the-art VertTIRP algorithm in terms of runtime on four benchmark datasets.

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Author information

Authors and Affiliations

  1. Shenzhen University, Shenzhen, China

    Philippe Fournier-Viger, Yuechun Li, M. Saqib Nawaz & Yulin He

  2. Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China

    Yulin He

Authors
  1. Philippe Fournier-Viger
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  2. Yuechun Li
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  3. M. Saqib Nawaz
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  4. Yulin He
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Corresponding author

Correspondence to Philippe Fournier-Viger .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology-Roorkee, Roorkee, India

    Partha Pratim Roy

  2. IBM Research, Gurugram, India

    Arvind Agarwal

  3. Southwest Jiaotong University, Chengdu, China

    Tianrui Li

  4. International Institute of Information Technology - Hyderabad, Hyderabad, India

    P. Krishna Reddy

  5. The University of Aizu, Fukushima, Japan

    R. Uday Kiran

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Fournier-Viger, P., Li, Y., Nawaz, M.S., He, Y. (2022). FastTIRP: Efficient Discovery of Time-Interval Related Patterns. In: Roy, P.P., Agarwal, A., Li, T., Krishna Reddy, P., Uday Kiran, R. (eds) Big Data Analytics. BDA 2022. Lecture Notes in Computer Science, vol 13773. Springer, Cham. https://doi.org/10.1007/978-3-031-24094-2_13

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  • DOI: https://doi.org/10.1007/978-3-031-24094-2_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24093-5

  • Online ISBN: 978-3-031-24094-2

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