The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Finding motifs using random projections
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting time series motifs under uniform scaling
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Finding Time Series Motifs in Disk-Resident Data
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Mining approximate motifs in time series
DS'06 Proceedings of the 9th international conference on Discovery Science
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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.