Anomalous Pattern Detection Using Context Aware Ubiquitous Data Mining

作者：Rehman Zahoor Ur^*; Shahbaz Muhammad; Shaheen Muhammad; Mehmood Sajid; Masood Syed Athar

来源：Life Science Journal-Acta Zhengzhou University Overseas Edition, 2012, 9(3): 6-12.

• stream mining
• context-aware
• anomalous pattern mining
• ubiquitous data mining

Due to the developments in technology number of applications emerged that produce huge amount of data in the form of streams. Dealing with this and extracting useful information from that data is a real challenge. In this paper, we have developed an architecture that can be used to manage data streaming applications and can extract useful information from that data in online fashion. To achieve mining results online, different phases in our model are parallelized. In this model we have also introduced the concept of context-awareness to improve performance of the proposed architectural model. In this model information from heterogeneous sources is gathered, fuse that information, and generate real-time results. These real-time results can be beneficial in different application area like web usage mining, online monitoring, fraud detection, network security, telecommunication calls monitoring, network monitoring and security, etc. To fulfill the objectives of this research, we incorporate lightweight online mining algorithms to extract useful but hidden information from the data gathered. Contextual information is exploited to detect anomalous behaviors. In this paper we have designed an architectural model to extract frequent patterns in the streaming data. [Zahoor ur Rehman, Muhammad Shahbaz, Muhammad Shaheen, Sajid Mehmood, Syed Athar Masood. Anomalous Pattern Detection Using Context Aware Ubiquitous Data Mining. Life Science Journal. 2012;9(3):6-12] (ISSN:1097-8135). http://www.lifesciencesite.com. 2

• 出版日期2012