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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References
Aggarwal, C.C.: Applications of frequent pattern mining. In: Aggarwal, C.C., Han, J. (eds.) Frequent Pattern Mining, pp. 443–467. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07821-2_18
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD, pp. 207–216 (1993)
Amphawan, K., Lenca, P., Surarerks, A.: Mining top-k periodic-frequent pattern from transactional databases without support threshold. In: Papasratorn, B., Chutimaskul, W., Porkaew, K., Vanijja, V. (eds.) IAIT 2009. CCIS, vol. 55, pp. 18–29. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10392-6_3
Anirudh, A., Kiran, R.U., Reddy, P.K., Kitsuregawa, M.: Memory efficient mining of periodic-frequent patterns in transactional databases. In: 2016 IEEE Symposium Series on Computational Intelligence, pp. 1–8 (2016)
Kiran, R.U., Kitsuregawa, M.: Novel techniques to reduce search space in periodic-frequent pattern mining. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014. LNCS, vol. 8422, pp. 377–391. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05813-9_25
Luna, J.M., Fournier-Viger, P., Ventura, S.: Frequent itemset mining: a 25 years review. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 9(6) (2019)
Tanbeer, S.K., Ahmed, C.F., Jeong, B.-S., Lee, Y.-K.: Discovering periodic-frequent patterns in transactional databases. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS (LNAI), vol. 5476, pp. 242–253. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01307-2_24
Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)
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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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