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3P-ECLAT: mining partial periodic patterns in columnar temporal databases

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Abstract

Partial periodic pattern (3P) mining is a vital data mining technique that aims to discover all interesting patterns that have exhibited partial periodic behavior in temporal databases. Previous studies have primarily focused on identifying 3Ps only in row temporal databases. One can not ignore the existence of 3Ps in columnar temporal databases as many real-world applications, such as Facebook and Adobe, employ them to store their big data. This paper proposes an efficient single database scan algorithm, Partial Periodic Pattern-Equivalence Class Transformation (3P-ECLAT), to identify all 3Ps in a columnar temporal database. The proposed algorithm compresses the given database into a novel list-based data structure and mines it recursively to find all 3Ps. The 3P-ECLAT leverages the “downward closure property” and “depth-first search technique” to reduce the search space and the computational cost. Extensive experiments have been conducted on synthetic and real-world databases to demonstrate the efficiency of the 3P-ECLAT algorithm. The memory and runtime results show that 3P-ECLAT outperforms its competitor considerably. Furthermore, 3P-ECLAT is highly scalable and is superior to the previous approach in handling large databases. Finally, to demonstrate the practical utility of our algorithm, we provide two real-world case studies, one on analyzing traffic congestion during disasters and another on identifying the highly polluted areas in Japan.

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Data Availability Statements

We have downloaded the above-mentioned databases from the well-known website [60]. We have also used two more real-world databases, Congestion and Drought. Unfortunately, we could not make these datasets public for confidentiality reasons.

Code Availability

To ensure the repeatability of our experiments, our algorithms were made available on GitHub [61]

Notes

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

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

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Funding

This research was funded by JSPS Kakenhi 21K12034.

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Authors and Affiliations

  1. The University of Aizu, Fukushima, Japan

    Uday Kiran Rage, Ravikumar Penugonda, Likhitha Palla & Yutaka Watanobe

  2. IIIT-Idupulapaya, Amaravati, RGUKT-AP, India

    Ravikumar Penugonda & Venus vikranth raj Bathala

  3. National Institute of Information and Communications Technology, Tokyo, Japan

    Uday Kiran Rage, Sadanori Ito & Koji Zettsu

  4. The University of Tokyo, Tokyo, Japan

    Uday Kiran Rage & Masashi Toyoda

  5. Department of Computer Science, Sri Balaji PG college, Affiliated to JNTU, Ananthapuram, 515001, Andhra Pradesh, India

    Veena Pamalla

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  1. Veena Pamalla
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Correspondence to Uday Kiran Rage.

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Pamalla, V., Rage, U.K., Penugonda, R. et al. 3P-ECLAT: mining partial periodic patterns in columnar temporal databases. Appl Intell 54, 657–679 (2024). https://doi.org/10.1007/s10489-023-05172-5

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