Summarization for Geographically Distributed Data Streams

We consider distributed computing environments where geo-referenced sensors feed a unique central server with numeric and uni-dimensional data streams. Knowledge discovery from these geographically distributed data streams poses several challenges including the requirement of data summarization in order to store the streamed data in a central server with a limited memory. We propose an enhanced segmentation algorithm in order to group data sources in the same spatial cluster if they stream data which evolve according to a close trajectory over the time. A trajectory is constructed by tracking only data points which represent a change of trend in the associated spatial cluster. Clusters of trajectories are discovered on-the-fly and stored in the database. Experiments prove effectiveness and accuracy of our approach.