Frequent Pattern Mining over Streaming Data: From models to research challenges

Research in frequent pattern mining from
streaming data becomes a pioneer in the field of information
systems. The data stream is a continuous flow of data generated
from different sources. Extracting frequent patterns from
streaming data raises new challenges for the data mining
community. We present an overview of the growing field of data
streams. Many applications handle streaming data such as
sensor networks, traffic management, log data, telephone call
records, and social networks. These applications generate high
volumes of streaming data with velocity, which is difficult to
handle with traditional data mining techniques. This paper
mainly reviewed different research algorithms, scientific
practices, and methods that have been developed for mining
frequent patterns from streaming data. In addition, it discusses
well-known open-source software and tools for data stream
mining, which are developing to handle streaming data. Finally,
it summarizes the open issues and challenges to current existing
approaches while handling and processing data streams in realworld applications.