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Towards Efficient Discovery of Stable Periodic Patterns in Big Columnar Temporal Databases

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Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence (IEA/AIE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13343))

Abstract

Extracting stable periodic-frequent patterns in very large temporal databases is a key task in big data analytics. Existing studies have mainly concentrated on discovering these patterns only in row temporal databases, and completely ignored the existence of these patterns in columnar databases, which are widely becoming popular for storing big data. In this paper we propose an efficient algorithm, Stable Periodic-frequent Pattern-Equivalence CLass Transformation (SPP-ECLAT), to find the desired patterns in a columnar temporal database. Empirical results demonstrate that the SPP-ECLAT algorithm is much faster and consumes significantly less memory than the state-of-the-art SPP-growth algorithm on sparse and dense databases.

H. N. Dao, P. Ravikumar, P. Likitha and B. V. V. Raj—These authors equally contributed to 98% of this paper.

Y. Watanobe and I. Paik—These authors have equally contributed to 2% of this paper.

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Notes

  1. 1.

    Row and columnar databases are also referred to as horizontal and vertical databases, respectively.

  2. 2.

    ACID stands for Atomicity, Consistency, Isolation, and Duration.

  3. 3.

    BASE stands for Basically Available, Soft state, and Eventually consistent.

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Author information

Authors and Affiliations

  1. The University of Aizu, Aizuwakamatsu, Fukushima, Japan

    Hong N. Dao, Penugonda Ravikumar, P. Likitha, Bathala Venus Vikranth Raj, R. Uday Kiran, Yutaka Watanobe & Incheon Paik

Authors
  1. Hong N. Dao
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  2. Penugonda Ravikumar
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  3. P. Likitha
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  4. Bathala Venus Vikranth Raj
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  5. R. Uday Kiran
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  6. Yutaka Watanobe
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  7. Incheon Paik
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Corresponding author

Correspondence to R. Uday Kiran .

Editor information

Editors and Affiliations

  1. i-SOMET, Inc., Morioka-shi, Iwate, Japan

    Hamido Fujita

  2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Philippe Fournier-Viger

  3. Texas State University, San Marcos, TX, USA

    Moonis Ali

  4. Shanghai University of Finance and Economics, Shanghai, China

    Yinglin Wang

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Cite this paper

Dao, H.N. et al. (2022). Towards Efficient Discovery of Stable Periodic Patterns in Big Columnar Temporal Databases. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_70

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  • DOI: https://doi.org/10.1007/978-3-031-08530-7_70

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08529-1

  • Online ISBN: 978-3-031-08530-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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