Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Dimensionality reduction for similarity searching in dynamic databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Indexing spatio-temporal trajectories with Chebyshev polynomials
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Atomic Wedgie: Efficient Query Filtering for Streaming Times Series
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Semi-supervised time series classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A decade of progress in indexing and mining large time series databases
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Indexable PLA for efficient similarity search
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
iSAX: indexing and mining terabyte sized time series
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A Dispersion-Based PAA Representation for Time Series
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 04
iSAX 2.0: Indexing and Mining One Billion Time Series
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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Similarity search on time series is an essential operation in many applications. In the state-of-the-art methods, such as the R-tree based methods, SAX and iSAX, time series are by default divided into equi-length segments globally, that is, all time series are segmented in the same way. Those methods then focus on how to approximate or symbolize the segments and construct indexes. In this paper, we make an important observation: global segmentation of all time series may incur unnecessary cost in space and time for indexing time series. We develop DSTree, a data adaptive and dynamic segmentation index on time series. In addition to savings in space and time, our new index can provide tight upper and lower bounds on distances between time series. An extensive empirical study shows that our new index DSTree supports time series similarity search effectively and efficiently.