| Frequent Pattern Mining over Streaming Data: From models to research challenges |
| Paper ID : 1042-ICCI2021 (R1) |
| Authors: |
|
Asmaa Saad Abdo *1, Rashed Salem2, Hatem Abdul-Kader3 1Information System Department, Faculty of Computers and Artificial Intelligence, University of Sadat City, Menoufia, Egypt 2Faculty of Computers and Information, Menoufia University 3Information System Department, Faculty of Computers and Information, Menoufia University El Menoufia, Egypt |
| Abstract: |
| 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 real-world applications. |
| Keywords: |
| Data streams, Frequent pattern mining, Stream data mining, Concept Drift, Window models |
| Status : Paper Accepted |
