Parallel and Distributed Search for Structure in Multivariate Time Series

. Efficient data mining algorithms are crucial for effective knowledge discovery. We present the Multi-Stream Dependency Detection (msdd) data mining algorithm that performs a systematic search for structure in multivariate time series of categorical data. The systematicity of msdd's search makes implementation of both parallel and distributed versions straightforward. Distributing the search for structure over multiple processors or networked machines makes mining of large numbers of databases or very large databases feasible. We present results showing that msdd efficiently finds complex structure in multivariate time series, and that the distributed version finds the same structure in approximately 1=n of the time required by msdd, where n is the number of machines across which the search is distributed. 1 Introduction Knowledge discovery in databases (KDD) is an iterative process in which data is repeatedly transformed and analyzed to reveal hidden structure. The analysis portio..