Paper Info
Title
Discovering time series motifs based on multidimensional index and early abandoning
Abstract
Time series motifs are pairs of previously unknown sequences in a time series database or subsequences of a longer time series which are very similar to each other. Since their formalization in 2002, discovering motifs has been used to solve problems in several application areas. In this paper, we propose a novel approach for discovering approximate motifs in time series. This approach is based on R*-tree and the idea of early abandoning. Our method is time and space efficient because it only saves Minimum Bounding Rectangles (MBR) of data in memory and needs a single scan over the entire time series database and a few times to read the original disk data in order to validate the results. The experimental results showed that our proposed algorithm outperforms the popular method, Random Projection, in efficiency.
Year
DOI
Venue
2012
10.1007/978-3-642-34630-9_8
ICCCI (1)
Keywords
Field
DocType
minimum bounding rectangles,longer time series,multidimensional index,novel approach,time series database,time series,discovering time series motif,entire time series database,random projection,popular method,original disk data,time series motif,r tree
Random projection,Multidimensional index,Data mining,R-tree,Computer science,Spacetime,Algorithm,Artificial intelligence,Time series database,Machine learning,Bounding overwatch
Conference
Volume
ISSN
Citations 
7653
0302-9743
1
PageRank 
References 
Authors
0.35
10
2
Name
Order
Citations
PageRank
Nguyen Thanh Son1357.03
Duong Tuan Anh25823.06