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

Dao, Hong N.; Ravikumar, Penugonda; Likhitha, Palla; Raj, Bathala Venus Vikranth; Kiran, R. Uday; Watanobe, Yutaka; Paik, Incheon

doi:10.1007/978-3-031-08530-7_70

Lecture Notes in Computer Science

2022

DOI: 10.1007/978-3-031-08530-7_70

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

Hong N. Dao

Penugonda Ravikumar

Palla Likhitha

et al.

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2023

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Cited by 3 publications

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“…Against this backdrop, we have extended the functionality of ECLAT [27] to mine stable periodic-frequent itemsets by introducing a new algorithm called Stable Periodic-frequent Pattern -Equivalence Class Transformation (SPP-ECLAT) in columnar temporal database. This paper is a substantially extended version of our conference paper [28] which reported a preliminary version of SPP-ECLAT. This paper extends the related work by extensively understanding the current literature, presenting the complexity analysis of our algorithm, and performing in-depth experiments studying the memory, runtime, and scalability of the mining algorithms.…”

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Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases

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Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a temporal database. Most previous studies focused on finding these itemsets in row (temporal) databases and disregarded the occurrences of these itemsets in columnar databases. Furthermore, the naïve approach of transforming a columnar database into a row database and then applying the existing algorithms to find interesting itemsets is not practicable due to computational reasons. With this motivation, this paper proposes a framework to discover stable periodic-frequent itemsets in columnar databases. Our framework employs a novel depth-first search algorithm that compresses a given columnar database into a unified dictionary and mines it recursively to find all stable periodic-frequent itemsets. The dictionary holds the information pertaining to itemsets and their temporal occurrences in a database. Experimental results on six databases demonstrate that the proposed algorithm is computationally efficient and scalable.INDEX TERMS Columnar databases, stable periodic-frequent itemset, itemset mining. I. INTRODUCTIONDatabase systems play a crucial role in storing the big data generated by real-world applications. Depending on the layout used for storing the data, one can broadly classify the databases into two types: row databases and columnar databases. 1 Row databases are primarily based on ACID 2 properties and organize the data as records by keeping the data associated with a record next to each other in a storage device. The popular row databases include MySQL [1] and Postgres [2]. In contrast, columnar databases are based on BASE 3 properties and organize data into fields and storeThe associate editor coordinating the review of this manuscript and approving it for publication was Laura Celentano .1 Columnar and row databases are referred as vertical and horizontal databases, respectively.2 ACID is an acronym for Atomicity, Consistency, Isolation, and Duration.3 BASE is an acronym for Basically Available, Soft state, and Eventually consistent.

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Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases

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A fundamental approach to discover closed periodic-frequent patterns in very large temporal databases

Pamalla,

Rage,

Penugonda

et al. 2023

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k-PFPMiner: Top-k Periodic Frequent Patterns in Big Temporal Databases

Likhitha,

Ravikumar,

Saxena

et al. 2023

IEEE Access

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Finding periodic-frequent patterns in temporal databases is a prominent data mining problem with bountiful applications. It involves discovering all patterns in a database that satisfy the user-specified minimum support (min_sup) and maximum periodicity (max_per) constraints. M in_sup controls the least number of transactions in which a pattern must appear in a database. M ax_per controls the maximum time interval within which a pattern must reappear in the database. The popular adoption of this task has been hindered by an open problem, which involves setting appropriate min_sup and max_per values for any given database. This paper addresses this open problem by proposing a solution to discover top-k periodicfrequent patterns in a temporal database. Top-k periodic-frequent patterns represent k periodic-frequent patterns with the lowest periodicity value in a database. An efficient depth-first search algorithm, Top-k Periodic-Frequent Pattern Miner (k-PFPMiner), which takes only k threshold as an input, was presented to find all desired patterns in a database. Experimental results on synthetic and real-world databases demonstrate that our algorithm is memory and runtime-efficient and also highly scalable.

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

Cited by 3 publications

References 13 publications

Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases

Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases

A fundamental approach to discover closed periodic-frequent patterns in very large temporal databases

k-PFPMiner: Top-k Periodic Frequent Patterns in Big Temporal Databases

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