Frequent pattern mining algorithms in fog computing environments: A systematic review

Recent advances in technology have resulted in generating or collecting massive volumes of data from rich data resources such as sensors and mobile devices in Internet of Things (IoT). Using data mining techniques can help overcome the mining problem in Fog computing environments which include millions of IoT devices. In addition, it can optimize response times, recourse consumption, and scalability in IoT applications. Frequent pattern mining, as one of the fundamental data mining tasks, is used for finding hidden patterns in such large datasets. The traditional data mining algorithms have many challenges such as scalability and resource consumption. This systematic review aimed to investigate the data mining algorithms, which focus on handling massive datasets, and present a technical taxonomy including the transaction-centric, item-centric, distributed, and parallel topics. The transaction-centric and MapReduce-based approaches were mostly utilized by 37% and 38%, respectively. Additionally, item-centric, distributed, and parallel algorithms were employed 12% and 13%, respectively. The response time as a Quality of Service (QoS) factor had the highest percentage in the estimations of data mining algorithms (55%), followed by scalability (25%), and cost (20%). To the best of our knowledge, no study has focused on fog-computing frequent pattern mining algorithms as one of the most important data mining tasks. This article aims to present a systematic review of the frequent pattern mining algorithms in fog computing and discuss the issues, challenges, and research perspectives for helping academia and industry leverage the power of data mining algorithms in fog computing.