Utility-Driven Data Analytics Algorithm for Transaction Modifications Using Pre-Large Concept With Single Database Scan

• Published in: IEEE transactions on big data Vol. 11; no. 5; pp. 2792 - 2808
• Main Authors: Yun, Unil, Kim, Hanju, Cho, Myungha, Ryu, Taewoong, Park, Seungwan, Kim, Doyoon, Kim, Doyoung, Lee, Chanhee, Pedrycz, Witold
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Big Data; Data analysis; Data mining; Data structures; Dynamic databases; high utility patterns; Pattern analysis; Pattern search; Performance evaluation; pre-large concept; Real time; Real-time systems; Runtime; single database scan; Statistical analysis; Streams; Synthetic data; transaction modifications; Upper bound
• ISSN: 2332-7790; 2372-2096
• DOI: 10.1109/TBDATA.2025.3556615

Utility-driven pattern analysis is a fundamental method for analyzing noteworthy patterns with high utility for diverse quantitative transactional databases. Recently, various approaches have emerged to handle large, dynamic database environments more efficiently by reducing the number of data scans and pattern expansion operations with the pre-large concept. However, existing pre-large-based high utility pattern mining methods either fail to handle real-time transaction modifications or require additional data scans to validate candidate patterns. In this paper, we propose a novel efficient utility-driven pattern mining algorithm using the pre-large concept for transaction modifications. Our method incorporates a single-scan-based framework through the management of actual utility values and discovers high utility patterns without candidate generation for efficient utility-driven dynamic data analysis in the modification environment. We compared the performance of the proposed method with state-of-the-art methods through extensive performance evaluation utilizing real and synthetic datasets. According to the evaluation results and a case study, the suggested method performs a minimum of 1.5 times faster than state-of-the-art methods alongside minimal compromise in memory, and it scaled well with increases in database size. Further statistical analyses indicate that the proposed method reduces the pattern search space compared to the previous method while delivering a complete set of accurate results without loss.