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Online Stochastic Pattern Matching

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Implementation and Application of Automata (CIAA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10977))

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Abstract

The pattern matching problem is to find all occurrences of a given pattern in an input text. In particular, we consider the case when the pattern is a stochastic regular language where each pattern string has its own probability. Our problem is to find all matching patterns—(start, end) indices in the text—whose probability is larger than a given threshold probability. A pattern matching procedure is frequently used on streaming data in several applications, and often it is very challenging to find the start index of a matching in streaming data. We design an efficient algorithm for the stochastic pattern matching problem over streaming data based on the transformation of the pattern PFA into a weighted automaton and a constant bound on the number of backtracks required to find a start index while reading the streaming input. We also employ heuristics that enable us to reduce the number of backtracks, which improves the practical runtime of our algorithm. We establish the tight theoretical runtime of the proposed algorithm and experimentally demonstrate its practical performance. Finally, we show a possible application of our algorithm to another stochastic pattern matching problem where we search for the maximum probability substring of a text that is a superstring of a specified string.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Yonsei University, Seoul, Republic of Korea

    Marco Cognetta & Yo-Sub Han

Authors
  1. Marco Cognetta
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  2. Yo-Sub Han
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Corresponding author

Correspondence to Yo-Sub Han .

Editor information

Editors and Affiliations

  1. University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada

    Cezar Câmpeanu

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Cognetta, M., Han, YS. (2018). Online Stochastic Pattern Matching. In: Câmpeanu, C. (eds) Implementation and Application of Automata. CIAA 2018. Lecture Notes in Computer Science(), vol 10977. Springer, Cham. https://doi.org/10.1007/978-3-319-94812-6_11

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  • DOI: https://doi.org/10.1007/978-3-319-94812-6_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94811-9

  • Online ISBN: 978-3-319-94812-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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