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
The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 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
Warping indexes with envelope transforms for query by humming
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Histogram distance for similarity search in large time series database
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
A review on time series data mining
Engineering Applications of Artificial Intelligence
Computers in Biology and Medicine
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Time series data are a ubiquitous data type appearing in many domains such as statistics, finance, multimedia, etc. Similarity search and measurement on time series data are typically different from on other data types since time series data have the associations among adjacent dimensions. Accordingly, the classic Euclidean distance metric is not an accurate similarity measure for time series. Therefore, Dynamic Time Warping (DTW) has become a better choice for similarity measurement on time series in various applications regardless of its high computational cost. To speed up the calculation, many research works attempt to speed up DTW calculation using indexing method, which always has a tradeoff between indexing efficiency and I/O cost. In this paper, we propose a novel method to balance this tradeoff under indexed sequential access using Sequentially Indexed Structure (SIS), an approach to time series indexing with low computational cost and small overheads on I/O. Finally, we conduct experiments to demonstrate our superiority in speed performance over the best existing method.