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BigSAM: Mining Interesting Patterns from Probabilistic Databases of Uncertain Big Data

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Book cover Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8643))

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Abstract

Nowadays, high volumes of valuable uncertain data can be easily collected or generated at high velocity in many real-life applications. Mining these uncertain Big data is computationally intensive due to the presence of existential probability values associated with items in every transaction in the uncertain data. Each existential probability value expresses the likelihood of that item to be present in a particular transaction in the Big data. In some situations, users may be interested in mining all frequent patterns from these uncertain Big data; in other situations, users may be interested in only a tiny portion of these mined patterns. To reduce the computation and to focus the mining for the latter situations, we propose a tree-based algorithm that (i) allows users to express the patterns to be mined according to their intention via the use of constraints and (ii) uses MapReduce to mine uncertain Big data for only those frequent patterns that satisfy user-specified constraints. Experimental results show the effectiveness of our algorithm in mining probabilistic databases of uncertain Big data.

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Acknowledgments

This project is partially supported by NSERC (Canada) and University of Manitoba.

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Authors and Affiliations

  1. University of Manitoba, Winnipeg, MB, Canada

    Fan Jiang, Carson Kai-Sang Leung & Richard Kyle MacKinnon

Authors
  1. Fan Jiang
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  2. Carson Kai-Sang Leung
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  3. Richard Kyle MacKinnon
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Corresponding author

Correspondence to Carson Kai-Sang Leung .

Editor information

Editors and Affiliations

  1. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

  2. Google Research, Mountain View, California, USA

    Haixun Wang

  3. University of Melbourne, Melbourne, Victoria, Australia

    James Bailey

  4. National Cheng Kung University, Tainan, Taiwan

    Vincent S. Tseng

  5. Japan Advanced Institute of Science and Technology, Nomi City, Japan

    Tu Bao Ho

  6. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  7. National Chengchi University, Taipei, Taiwan

    Arbee L.P. Chen

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© 2014 Springer International Publishing Switzerland

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Jiang, F., Leung, C.KS., MacKinnon, R.K. (2014). BigSAM: Mining Interesting Patterns from Probabilistic Databases of Uncertain Big Data. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_70

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  • DOI: https://doi.org/10.1007/978-3-319-13186-3_70

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13185-6

  • Online ISBN: 978-3-319-13186-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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