In recent years, HUIM (or a.k.a. high-utility itemset mining) can be seen as investigated in an extensive manner and studied in many applications especially in basket-market analysis and its relevant applications. Since current basket-market scenario also involves IoT equipment to collect information, i.e., sensor or smart devices, it is necessary to consider the mining of HUIs (or a.k.a. high-utility itemsets) in a large-scale database especially with IoT situations. First, a GA-based MapReduce model is presented in this work known as GMR-Miner for mining closed patterns with high utilization in large-scale databases. The -means model is initially adopted to group transactions regarding their relevant correlation based on the frequency factor. A genetic algorithm (GA) is utilized in the developed MapReduce framework that can be used to explore the potential and possible candidates in a limited time. Also, the developed 3-tier MapReduce model can be easily deployed in Spark for the handlings of any database of large scale for knowledge discovery of closed patterns with high utilization. We created sets of extensive experimental environments for evaluating the results of the developed GMR-Miner compared to the well-known and state-of-the-art CLS-Miner. We present our in-depth results to show that the developed GMR-Miner outperforms CLS-Miner in many criteria, i.e., memory usage, scalability, and runtime.

中文翻译：

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在大规模物联网环境中挖掘有利可图的简洁模式

近年来，HUIM（或又名高效项目集挖掘）可以被视为以广泛的方式进行研究，并在许多应用中进行了研究，特别是在篮子市场分析及其相关应用中。由于当前的篮子市场场景还涉及物联网设备来收集信息，即传感器或智能设备，因此有必要考虑在大型数据库中挖掘 HUI（或又名高效能项集），尤其是在物联网情况下。首先，在这项工作中提出了一种基于 GA 的 MapReduce 模型，称为 GMR-Miner，用于挖掘大规模数据库中具有高利用率的封闭模式。在-最初采用均值模型根据频率因素对交易的相关性进行分组。开发的 MapReduce 框架中使用了遗传算法 (GA)，可用于在有限的时间内探索潜在和可能的候选者。此外，开发的 3 层 MapReduce 模型可以轻松部署在 Spark 中，用于处理任何大规模数据库，以实现高利用率的封闭模式的知识发现。我们创建了一组广泛的实验环境，用于评估开发的 GMR-Miner 与众所周知的最先进的 CLS-Miner 的结果。我们展示了我们的深入结果，以表明开发的 GMR-Miner 在许多标准上都优于 CLS-Miner，即内存使用、可扩展性和运行时间。