Discovering Trend-Based Clusters in Spatially Distributed Data Streams

Many emerging applications are characterized by real-time stream data acquisition through sensors which have geographical locations and/or spatial extents. Streaming prevents from storing all data from the stream and performing multiple scans of the entire data sets as normally done in traditional applications. The drift of data distribution poses additional challenges to the spatio-temporal data mining techniques. We address these challenges for a class of spatio-temporal patterns, called trend-clusters, which combine the semantics of both clusters and trends in spatio-temporal environments. We propose an algorithm to interleave spatial clustering and trend discovery in order to continuously cluster geo-referenced data which vary according to a similar trajectory (trend) in the recent past (window time). An experimental study demonstrates the effectiveness of our algorithm.