Prelarge-Based Utility-Oriented Data Analytics for Transaction Modifications in Internet of Things

The Internet of Things (IoT) environment includes things that exchange information using data generated through sensors. The IoT technology can be executed efficiently by providing the necessary information to things instead of transmitting all the data. There is a need for an algorithm that can extract meaningful information for the things from the data. High utility pattern mining, which can handle the characteristics of real-world databases better than traditional pattern mining methods, has been actively researched. The traditional high utility pattern mining techniques find meaningful patterns from static databases. Therefore, these techniques are not suitable for the dynamically changing databases in the real world. In order to solve this problem, a variety of methods that consider the modifications, deletions, and insertions of transactions are proposed. The prelarge concept, which can be efficiently operated by reducing the rescanning of the original database using two thresholds, was also proposed in order to handle the limitations of the methods that are used for static databases. In the prelarge concept, the large patterns and the prelarge patterns that are discovered are maintained and used for the next transaction change. In this article, we proposed a new method pre-large based incremental high utility pattern mining for transaction modification (PIHUP-MOD) implemented as a tree structure in order to handle the transaction modifications. The method has an efficient structure and techniques to mine high utility patterns in the transaction modifications with the prelarge concept. We conducted the performance evaluation, and the experimental results on the real datasets and the synthetic datasets showed that the proposed approach has a better performance than the state-of-the-art approaches in terms of the runtime, memory usage, and scalability.