Abstract
Finding periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Most previous studies focused on finding these patterns in row temporal databases. To the best of our knowledge, there exists no study that aims to find periodic-frequent patterns in columnar temporal databases. One cannot ignore the importance of the knowledge that exists in very large columnar temporal databases. It is because the real-world big data is widely stored in columnar temporal databases. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world databases demonstrate that PF-ECLAT is not only memory and runtime efficient but also highly scalable. Finally, we present the usefulness of PF-ECLAT with a case study on air pollution analytics.
First three authors have equally contributed to 90% of the paper. Remaining author has contributed to 10% of the paper.
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Penugonda, R., Palla, L., Rage, U.K., Watanobe, Y., Zettsu, K. (2021). Towards Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12798. Springer, Cham. https://doi.org/10.1007/978-3-030-79457-6_3
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