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
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
Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Skyline Index for Time Series Data
IEEE Transactions on Knowledge and Data Engineering
An Improvement of PIP for Time Series Dimensionality Reduction and Its Index Structure
KSE '10 Proceedings of the 2010 Second International Conference on Knowledge and Systems Engineering
Time series subsequence searching in specialized binary tree
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
A novel bit level time series representation with implication of similarity search and clustering
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Subsequence matching is a non-trivial task in time series data mining. In this paper, we introduce our proposed approach for solving subsequence matching which is based on IPIP, our new method for time series dimensionality reduction. The IPIP method is a combination of PIP (Perceptually Important Points) method and clipping technique in order that the new method not only satisfies the lower bounding condition, but also provides a bit level representation for time series. Furthermore, we can make IPIP indexable by showing that a time series compressed by IPIP can be indexed with the support of Skyline index. Our experiments show that our IPIP method is better than PAA in terms of tightness of lower bound and pruning power, and in subsequence matching, IPIP with Skyline index can perform faster than PAA based on traditional R*- tree.