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
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
An Index-Based Approach for Similarity Search Supporting Time Warping in Large Sequence Databases
Proceedings of the 17th International Conference on Data Engineering
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
Warping indexes with envelope transforms for query by humming
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
A Subsequence Matching Algorithm that Supports Normalization Transform in Time-Series Databases
Data Mining and Knowledge Discovery
Exact indexing of dynamic time warping
Knowledge and Information Systems
FTW: fast similarity search under the time warping distance
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Structural Periodic Measures for Time-Series Data
Data Mining and Knowledge Discovery
A Bit Level Representation for Time Series Data Mining with Shape Based Similarity
Data Mining and Knowledge Discovery
Characteristic-Based Clustering for Time Series Data
Data Mining and Knowledge Discovery
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
The TS-tree: efficient time series search and retrieval
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Proceedings of the VLDB Endowment
Boundary-based lower-bound functions for dynamic time warping and their indexing
Information Sciences: an International Journal
Shape-based template matching for time series data
Knowledge-Based Systems
How many reference patterns can improve profitability for real-time trading in futures market?
Expert Systems with Applications: An International Journal
Shape-Based clustering for time series data
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Among many existing distance measures for time series data, Dynamic Time Warping (DTW) distance has been recognized as one of the most accurate and suitable distance measures due to its flexibility in sequence alignment. However, DTW distance calculation is computationally intensive. Especially in very large time series databases, sequential scan through the entire database is definitely impractical, even with random access that exploits some index structures since high dimensionality of time series data incurs extremely high I/O cost. More specifically, a sequential structure consumes high CPU but low I/O costs, while an index structure requires low CPU but high I/O costs. In this work, we therefore propose a novel indexed sequential structure called TWIST (Time Warping in Indexed Sequential sTructure) which benefits from both sequential access and index structure. When a query sequence is issued, TWIST calculates lower bounding distances between a group of candidate sequences and the query sequence, and then identifies the data access order in advance, hence reducing a great number of both sequential and random accesses. Impressively, our indexed sequential structure achieves significant speedup in a querying process. In addition, our method shows superiority over existing rival methods in terms of query processing time, number of page accesses, and storage requirement with no false dismissal guaranteed.