NDLI: Indexing Multidimensional Time-Series

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Indexing Multidimensional Time-Series

Content Provider	Springer Nature Link
Author	Vlachos, Michail Hadjieleftheriou, Marios Gupulos, Dimitrios Keogh, Eamonn
Copyright Year	2006
Abstract	While most time series data mining research has concentrated on providing solutions for a single distance function, in this work we motivate the need for an index structure that can support multiple distance measures. Our specific area of interest is the efficient retrieval and analysis of similar trajectories. Trajectory datasets are very common in environmental applications, mobility experiments, and 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.
Starting Page	1
Ending Page	20
Page Count	20
File Format	PDF
ISSN	10668888
Journal	The VLDB Journal
Volume Number	15
Issue Number	1
e-ISSN	0949877X
Language	English
Publisher	Springer-Verlag
Publisher Date	2005-07-22
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ensemble index Longest common subsequence Dynamic time warping Trajectories Motion capture Database Management
Content Type	Text
Resource Type	Article
Subject	Information Systems Hardware and Architecture

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