There is growing interest in algorithms for processing and querying continuous data streams (i.e., data that is seen only once in a fixed order) with limited memory resources. In i...
Sumit Ganguly, Minos N. Garofalakis, Rajeev Rastog...
Three methods for combining multiple clustering systems are presented and evaluated, focusing on the problem of finding the correspondence between clusters of different systems. ...
There is growing interest in run-time detection as parallel and distributed systems grow larger and more complex. This work targets run-time analysis of complex, interactive scien...
This paper describes a parallel algorithm for correlating or “fusing” streams of data from sensors and other sources of information. The algorithm is useful for applications w...
We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the ...