document: Indexing multi-dimensional time-series with support for multiple distance measures Although most time-series data mining research has concentrated on providing solutions for a single distance function , in this work we motivate the need for a single index structure that can support multiple distance measures . Our specific area of interest is the efficient retrieval and analysis of trajectory similarities . Trajectory datasets are very common in environmental applications , mobility experiments , video surveillance and are especially important for the discovery of certain biological patterns . Our primary similarity measure is based on the Longest Common Subsequence ( LCSS ) model , that offers enhanced robustness , particularly for noisy data , which are encountered very often in real world applications . However , our index is able to accommodate other distance measures as well , including the ubiquitous Euclidean distance , and the increasingly popular Dynamic Time Warping ( DTW ) . While other researchers have advocated one or other of these similarity measures , a major contribution of our work is the ability to support all these measures without the need to restructure the index . Our framework guarantees no false dismissals and can also be tailored to provide much faster response time at the expense of slightly reduced precision\/recall . The experimental results demonstrate that our index can help speed-up the computation of expensive similarity measures such as the LCSS and the DTW .
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