Fast Subsequence Matching in Time-Series Databases

Fast Subsequence Matching in Time-Series Databases (1994)

by Christos Faloutsos , M. Ranganathan , Yannis Manolopoulos

Venue:	SIGMOD 94
Citations:	372 - 18 self

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Datum	Value	Source
TITLE	Fast Subsequence Matching in Time-Series Databases	user correction
AUTHOR NAME	Christos Faloutsos	user correction
AUTHOR NAME	M. Ranganathan	user correction
AUTHOR NAME	Yannis Manolopoulos	user correction
ABSTRACT	We present an efficient indexing method to locate 1-dimensional subsequences witbin a collection of sequences, such that the subsequences match a given (query) pattern within a specified tolerance. The idea is to map each data sequence into a small set of multidimensional rectangles in feature space. Then, these rectangles can be readily indexed using traditional spatial access methods, like the R*-tree [9]. In more deteil, we use a sliding window over the data sequence and extract its features; the result is a trail in feature space. We propose an efficient and effective algorithm to divide such trails into sub-trails, which are subsequently represented by their Minimum Bounding Rectangles (MBRs). We also examine queries of varying lengths, and we show how to handle each case efficiently. We implemented our method and carried out experiments on synthetic and real data (stock price movements). We compared the method to sequential scanning, which is the only obvious competitor. The results were excellent: our method accelerated the search time from 3 times up to 100 times.	user correction
YEAR	1994	user correction
VENUE	SIGMOD 94	user correction
CITATIONS	31 found	ParsCit 1.0