Toward visual analysis of ensemble data sets
Proceedings of the 2009 Workshop on Ultrascale Visualization
Continuously identifying representatives out of massive streams
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Prediction-based geometric monitoring over distributed data streams
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
ACM Transactions on Knowledge Discovery from Data (TKDD)
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We investigate the problem of clustering on distributed data streams. In particular, we consider the k-median clustering on stream data arriving at distributed sites which communicate through a routing tree. Distributed clustering on high speed data streams is a challenging task due to limited communication capacity, storage space, and computing power at each site. In this paper, we propose a suite of algorithms for computing (1 + epsiv) -approximate k-median clustering over distributed data streams under three different topology settings: topology-oblivious, height-aware, and path-aware. Our algorithms reduce the maximum per node transmission to polylog N (opposed to Omega(N) for transmitting the raw data). We have simulated our algorithms on a distributed stream system with both real and synthetic datasets composed of millions of data. In practice, our algorithms are able to reduce the data transmission to a small fraction of the original data. Moreover, our results indicate that the algorithms are scalable with respect to the data volume, approximation factor, and the number of sites.