An innovative framework for supporting frequent pattern mining problems in IoT environments

Abstract

In the current era of big data, high volumes of a wide variety of data of different veracity can be easily generated or collected at a high velocity from rich sources of data include devices from the Internet of Things (IoT). Embedded in these big data are useful information and valuable knowledge. Hence, frequent pattern mining and its related research problem of association rule mining, which aim to discover implicit, previously unknown and potentially useful information and knowledge—in the form of sets of frequently co-occurring items or rules revealing relationships between these frequent sets—from these big data have drawn attention of many researchers. For instance, since introduction of the research problems of association rule mining or frequent pattern mining, numerous information system and engineering approaches have been developed. These include the development of serial algorithms, distributed and parallel algorithms, as well as MapReduce-based big data mining algorithms. These algorithms can be run in local computers, distributed and parallel environments, as well as clusters, grids and clouds. In this paper, we describe some of these algorithms and discuss how to mine frequent patterns or association rules in fogs—i.e., edges of the computing network.