A Multi-metric Index for Euclidean and Periodic Matching

Abstract

Abstract. In many classification and data-mining applications the user does not know a priori which distance measure is the most appropriate for the task at hand without examining the produced results. Also, in several cases, different distance functions can provide diverse but equally intuitive results (according to the specific focus of each measure). In order to address the above issues, we elaborate on the construction of a hybrid index structure that supports query-by-example on shape and structural distance measures, therefore lending enhanced exploratory power to the system user. The shape distance measure that the index supports is the ubiquitous Euclidean distance, while the structural distance measure that we utilize is based on important periodic features extracted from a sequence. This new measure is phase-invariant and can provide flexible sequence characterizations, loosely resembling the Dynamic Time Warping, requiring only a fraction of the computational cost of the latter. Exploiting the relationship between the Euclidean and periodic measure, the new hybrid index allows for powerful query processing, enabling the efficient answering of kNN queries on both measures in a single index scan. We envision that our system can provide a basis for fast tracking of correlated time-delayed events, with applications in data visualization, financial market analysis, machine monitoring/diagnostics and gene expression data analysis.