Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Efficient Indexing of Spatiotemporal Objects
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Complex spatio-temporal pattern queries
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Elastic partial matching of time series
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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In recent years, there has been an increasing interest in the detection of non-contiguous sequence patterns in data streams. Existing works define a fixed temporal constraint between every pair of adjacent elements of the sequence. While this method is simple and intuitive, it suffers from the following shortcomings: 1)It is difficult for the users who are not domain experts to specify such complex temporal constraints properly; 2)The fixed temporal constraint is not flexible to capture interested patterns hidden in long sequences. In this paper, we introduce a novel type of non-contiguous sequence pattern, named Elastic Temporal Constrained Non-contiguous Sequence Pattern(ETC-NSP). Such a pattern defines an elastic temporal constraint on the sequence, thus is more flexible and effective as opposed to the fixed temporal constraints. Detection of ETC-NSP in data streams is a non-trivial task since a brute force approach is exponential in time. Our method exploits an similarity measurement called Minimal Variance Matching as the basic matching mechanism. To further speed up the monitoring process, we develop pruning strategies which make it practical to use ETC-NSP in streaming environment. Experimental studies show that the proposed method is efficient and effective in detecting non-contiguous sequence patterns from data streams.