Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
Fundamentals of speech recognition
Fundamentals of speech recognition
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
General match: a subsequence matching method in time-series databases based on generalized windows
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Querying Time Series Data Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
Duality-Based Subsequence Matching in Time-Series Databases
Proceedings of the 17th International Conference on Data Engineering
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Optimizing Multi-Feature Queries for Image Databases
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
Towards Efficient Multi-Feature Queries in Heterogeneous Environments
ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
WARP: Accurate Retrieval of Shapes Using Phase of Fourier Descriptors and Time Warping Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
RankSQL: query algebra and optimization for relational top-k queries
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
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
Optimizing top-k queries for middleware access: A unified cost-based approach
ACM Transactions on Database Systems (TODS)
Progressive and selective merge: computing top-k with ad-hoc ranking functions
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Ranked subsequence matching in time-series databases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Approximate embedding-based subsequence matching of time series
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Anticipatory DTW for efficient similarity search in time series databases
Proceedings of the VLDB Endowment
A new approach for processing ranked subsequence matching based on ranked union
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
A new approach for processing ranked subsequence matching based on ranked union
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
An envelope-based approach to rotation-invariant boundary image matching
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
A generic framework for efficient and effective subsequence retrieval
Proceedings of the VLDB Endowment
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Ranked subsequence matching finds top-k subsequences most similar to a given query sequence from data sequences. Recently, Han et al. [12] proposed a solution (referred to here as HLMJ) to this problem by using the concept of the minimum distance matching window pair (MDMWP) and a global priority queue. By using the concept of MDMWP, HLMJ can prune many unnecessary accesses to data subsequences using a lower bound distance. However, we notice that HLMJ may incur serious performance overhead for important types of queries. In this paper, we propose a novel systematic framework to solve this problem by viewing ranked subsequence matching as ranked union. Specifically, we propose a notion of the matching subsequence equivalence class (MSEQ) and a novel lower bound called the MSEQ-distance. To completely eliminate the performance problem of HLMJ, we also propose a cost-aware density-based scheduling technique, where we consider both the density and cost of the priority queue. Extensive experimental results with many real datasets show that the proposed algorithm outperforms HLMJ and the adapted PSM [22], a state-of-the-art index-based merge algorithm supporting non-monotonic distance functions, by up to two to three orders of magnitude, respectively.