document: Correlating synchronous and asynchronous data streams In a variety of modern mining applications , data are commonly viewed as infinite time ordered data streams rather as finite data sets stored on disk . This view challenges fundamental assumptions commonly made in the context of several data mining algorithms . In this paper , we study the problem of identifying correlations between multiple data streams . In particular , we propose algorithms capable of capturing correlations between multiple continuous data streams in a highly efficient and accurate manner . Our algorithms and techniques are applicable in the case of both synchronous and asynchronous data streaming environments . We capture correlations between multiple streams using the well known technique of Singular Value Decomposition ( SVD ) . Correlations between data items , and the SVD technique in particular , have been repeatedly utilized in an off-line ( non stream ) data mining problems , for example forecasting , approximate query answering , and data reduction . We propose a methodology based on a combination of dimensionality reduction and sampling to make the SVD technique suitable for a data stream context . Our techniques are approximate , trading accuracy with performance , and we analytically quantify this tradeoff . We present a through experimental evaluation , using both real and synthetic data sets , from a prototype implementation of our technique , investigating the impact of various parameters in the accuracy of the overall computation . Our results indicate , that correlations between multiple data streams can be identified very efficiently and accurately . The algorithms proposed herein , are presented as generic tools , with a multitude of applications on data stream mining problems .
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