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Australasian Conference on Data Mining

AusDM 2017: Data Mining pp 144–157Cite as

An Incremental Anytime Algorithm for Mining T-Patterns from Event Streams

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 845))

Abstract

Temporal patterns that capture frequent time differences occurring between items in a sequence are gaining increasing attention as a growing research area. Time-interval sequential patterns (also known as T-Patterns) not only capture the order of symbols but also the time delay between symbols, where the time delay is specified as a time-interval between a pair of symbols. Such patterns have been shown to be present in many different types of data (e.g. spike data, smart home activity, DNA sequences, human and animal behaviour analysis and the like) which cannot be captured by other pattern types. Recently, several mining algorithms have been proposed to mine such patterns from either transaction databases or static sequences of time-stamped events. However, they are not capable of online mining from streams of time-stamped events (i.e. event streams). An increasingly common form of data, event streams bring more challenges as they are often unsegmented and with unobtainable total size. In this paper, we propose a mining algorithm that discovers time-interval patterns online, from event streams and demonstrate its capability on a benchmark synthetic dataset.

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Notes

  1. 1.

    Available from http://www.snl.salk.edu/~fellous/data/JN2004data/data.html.

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Author information

Authors and Affiliations

  1. School of Computer Science and Software Engineering, The University of Western Australia, Crawley, Australia

    Keith Johnson & Wei Liu

Authors
  1. Keith Johnson
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  2. Wei Liu
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Corresponding author

Correspondence to Keith Johnson .

Editor information

Editors and Affiliations

  1. RMIT University, Melbourne, Victoria, Australia

    Yee Ling Boo

  2. University of Wollongong, Wollongong, New South Wales, Australia

    David Stirling

  3. La Trobe University, Melbourne, Victoria, Australia

    Lianhua Chi

  4. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia, Australia

    Lin Liu

  5. La Trobe University, Melbourne, Victoria, Australia

    Kok-Leong Ong

  6. Microsoft Pty Ltd, Singapore, Singapore

    Graham Williams

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© 2018 Springer Nature Singapore Pte Ltd.

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Cite this paper

Johnson, K., Liu, W. (2018). An Incremental Anytime Algorithm for Mining T-Patterns from Event Streams. In: Boo, Y., Stirling, D., Chi, L., Liu, L., Ong, KL., Williams, G. (eds) Data Mining. AusDM 2017. Communications in Computer and Information Science, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-13-0292-3_9

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  • DOI: https://doi.org/10.1007/978-981-13-0292-3_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0291-6

  • Online ISBN: 978-981-13-0292-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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