Pre-Eminent Utility Driven Data Analytics Based on Prelarge Patterns for Dynamic Transaction Deletion in IoT Environments

On the Internet of Things (IoT) environment, interconnected devices continuously share generated data in real time, typically collected with a specific purpose. To ensure efficiency, IoT technology must deliver only the most relevant insights into these devices. High utility pattern mining is a technique that extracts important knowledge, and there has been research on performance improvements to efficiently mine these patterns in dynamic environments. Although approaches with a prelarge concept have been introduced to mining high utility patterns in data deletion environments, the state-of-the-art method relies on inefficient data structures, making them unsuitable for real-time analysis with IoT data. To overcome these limitations, this article proposes a novel utility pattern mining approach with prelarge concept for dynamic IoT environments, where data is deleted because of sensor errors or storage constraints. The actual utilities of the prelarge patterns are maintained to skip the verification and improve communication delays. It can optimize processing time and memory consumption due to storing fewer large or prelarge patterns based on the actual utility. The proposed method operates in an efficient list-based manner, enabling effective search space pruning when the rescan condition is met and generating compact data structures through transaction merging. The experiments indicate that our algorithm is outstanding regarding processing time and scalability with minimal compromise in memory consumption compared with the existing methods, while extracting the exact patterns. Additionally, an analysis that replicates real IoT environments demonstrates that the proposed method is sufficiently applicable in real-world settings.