Proceedings ArticleDOI
Prefix-querying: an approach for effective subsequence matching under time warping in sequence databases
Sanghyun Park,Sang-Wook Kim,June-Suh Cho,Sriram Padmanabhan +3 more
- pp 255-262
TLDR
The prefix-querying approach based on sliding windows is incorporated, which provides effective and scalable subsequence matching even with a large volume of a database and achieves significant speedup with real-world S&P 500 stock data and with very large synthetic data.Abstract:
This paper discusses an index-based subsequence matching that supports time warping in large sequence databases. Time warping enables finding sequences with similar patterns even when they are of different lengths. In our earlier work, we suggested an efficient method for whole matching under time warping. This method constructs a multi-dimensional index on a set of feature vectors, which are invariant to time warping, from data sequences. For filtering at feature space, it also applies a lower-bound function, which consistently underestimates the time warping distance as well as satisfies the triangular inequality.In this paper, we incorporate the prefix-querying approach based on sliding windows into the earlier approach. For indexing, we extract a feature vector from every subsequence inside a sliding window and construct a multi-dimensional index using a feature vector as indexing attributes. For query processing, we perform a series of index searches using the feature vectors of qualifying query prefixes. Our approach provides effective and scalable subsequence matching even with a large volume of a database. We also prove that our approach does not incur false dismissal. To verify the superiority of our method, we perform extensive experiments. The results reveal that our method achieves significant speedup with real-world S&P 500 stock data and with very large synthetic data.read more
Citations
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Journal ArticleDOI
A review on time series data mining
TL;DR: The primary objective of this paper is to serve as a glossary for interested researchers to have an overall picture on the current time series data mining development and identify their potential research direction to further investigation.
Proceedings ArticleDOI
Optimization of subsequence matching under time warping in time-series databases
TL;DR: In this paper, a method that eliminates all possible redundant calculations in the CPU processing step was proposed to improve the performance of subsequence matching under time warping in time-series databases.
Optimization of Subsequence Matching Under Time-Warping in Time-Series Databases
Man-Soon Kim,Sang-Wook Kim +1 more
TL;DR: The experimental results showed that the proposed method can make great improvement in performance of subsequence matching under time warping, and Naive-Scan, which has been known to show the worst performance, performs much better than LB-Scan as well as ST-Filter in all the cases by employing the proposed methods for CPU processing.
Efficient subsequence matching for sequence database under time warping
TL;DR: This paper presents a method that supports dynamic time warping for subsequence matching within a collection of sequences and takes full advantage of the "sliding window" approach and can handle queries of arbitrary length.
Journal ArticleDOI
A segment-wise time warping method for time scaling searching
Mi Zhou,Man Hon Wong +1 more
TL;DR: Through different experiments, it is found that the index can greatly reduce the amount of data that must be retrieved, and will lead to great improvements of performance in large sequence database compared with a sequential search.
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