Temporal range exploration of large scale multidimensional time series data

Abstract

We consider the problem of querying large scale multidimensional time series data to discover events of interest, test and validate hypotheses, or to associate temporal patterns with specific events. Large amounts of multidimensional time series data are currently available, and this type of data is growing at a fast rate due to the current trends in collecting time series of business, scientific, demographic, and simulation data. The ability to explore such collections interactively, even at a coarse level, will be critical in discovering the information and knowledge embedded in such collections. We develop indexing techniques and search algorithms to efficiently handle temporal range value querying of multidimensional time series data. Our indexing uses linear space data structures that enable the handling of queries very efficiently, invoking in the worst case a logarithmic number of queries to single time slices. We also show that our algorithm is ideally suited for parallel implementation on clusters of processors achieving a linear speedup in the number of available processors. A particularly simple data structure with provably good bounds is also presented for the case when the number of multidimensional objects is relatively small. These techniques improve significantly over previous techniques for either the serial or the parallel case, and are evaluated by extensive experimental results that confirm their superior performance.