Neighbor-based pattern detection for windows over streaming data

The discovery of complex patterns such as clusters, outliers, and associations from huge volumes of streaming data has been recognized as critical for many domains. However, pattern detection with sliding window semantics, as required by applications ranging from stock market analysis to moving object tracking , remains largely unexplored. Applying static pattern detection algorithms from scratch to every window is prohibitively expensive due to their high algorithmic complexity. This work tackles this problem by developing the first solution for incremental detection of neighbor-based patterns specific to sliding window scenarios. The specific pattern types covered in this work include density-based clusters and distance-based outliers. Incremental pattern computation in highly dynamic streaming environments is challenging, because purging a large amount of to-be-expired data from previously formed patterns may cause complex pattern changes including migration, splitting, merging ...

• Type: Conference
• Year: 2009
• Where: EDBT
• Authors: Di Yang, Elke A. Rundensteiner, Matthew O. Ward
• Keywords: EDBT 2009, Pattern Detection, Pattern Detection Algorithms, Sliding Windows