Classification spanning correlated data streams

In many applications, classifiers need to be built based on multiple related data streams. For example, stock streams and news streams are related, where the classification patterns may involve features from both streams. Thus instead of mining on a single isolated stream, we need to examine multiple related data streams in order to find such patterns and build an accurate classifier. Other examples of related streams include traffic reports and car accidents, sensor readings of different types or at different locations, etc. In this paper, we consider the classification problem defined over sliding-window join of several input data streams. As the data streams arrive in fast pace and the many-to-many join relationship blows up the data arrival rate even more, it is impractical to compute the join and then build the classifier each time the window slides forward. We present an efficient algorithm to build a Na

• Type: Conference
• Year: 2006
• Where: CIKM
• Authors: Yabo Xu, Ke Wang, Ada Wai-Chee Fu, Rong She, Jian Pei
• Keywords: CIKM 2006, Data Streams, Information Management, Multiple Related Data, Related Data Streams