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International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research

CPAIOR 2018: Integration of Constraint Programming, Artificial Intelligence, and Operations Research pp 82–99Cite as

EpisodeSupport: A Global Constraint for Mining Frequent Patterns in a Long Sequence of Events

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10848))

Abstract

The number of applications generating sequential data is exploding. This work studies the discovering of frequent patterns in a large sequence of events, possibly time-stamped. This problem is known as the Frequent Episode Mining (FEM). Similarly to the mining problems recently tackled by Constraint Programming (CP), FEM would also benefit from the modularity offered by CP to accommodate easily additional constraints on the patterns. These advantages do not offer a guarantee of efficiency. Therefore, we introduce two global constraints for solving FEM problems with or without time consideration. The time-stamped version can accommodate gap and span constraints on the matched sequences. Our experiments on real data sets of different levels of complexity show that the introduced constraints is competitive with the state-of-the-art methods in terms of execution time and memory consumption while offering the flexibility of adding constraints on the patterns.

J. O. R. Aoga—This author is supported by the FRIA-FNRS, Belgium.

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Notes

  1. 1.

    https://bitbucket.org/projetsJOHN/episodesupport (also available in [31]).

  2. 2.

    Results provided in [14] are directly used since the implementation is not available.

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Authors and Affiliations

  1. Université catholique de Louvain, Louvain-La-Neuve, Belgium

    Quentin Cappart, John O. R. Aoga & Pierre Schaus

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  1. Quentin Cappart
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  2. John O. R. Aoga
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  3. Pierre Schaus
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Correspondence to Quentin Cappart .

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  1. Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Willem-Jan van Hoeve

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Cappart, Q., Aoga, J.O.R., Schaus, P. (2018). EpisodeSupport: A Global Constraint for Mining Frequent Patterns in a Long Sequence of Events. In: van Hoeve, WJ. (eds) Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2018. Lecture Notes in Computer Science(), vol 10848. Springer, Cham. https://doi.org/10.1007/978-3-319-93031-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-93031-2_7

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