Abstract
Sequential data segments in data streams are very meaningful in many areas. These data segments usually have complicated appearance and require online processing. But matching these data segments can be time-consuming and there are multiple matching tasks to be proceeded simultaneously. This paper presents a novel data structurepattern combination graph (PCG) and corresponding algorithms to accomplish composite pattern matching over data streams. To make it possible to deal with complicated patterns efficiently, PCG firstly identify similar segments among different segments as basic patterns, and then deal with the composite semantics between basic patterns. In this way, data stream flow into PCG for matching in the form of basic patterns. Later procedures are operated according to the types of nodes in PCG and the final results are returned to users. From the perspective of recall ratio, precision ratio and efficiency, the experimental results on real data sets of medical streams show that PCG is feasible and effective.
This work was supported by Natural Science Foundation of China (No.60973002 and No.61170003), the National High Technology Research and Development Program of China (Grant No. 2012AA011002), National Science and Technology Major Program (Grant No. 2010ZX01042-002-002-02, 2010ZX01042-001-003-05).
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Ju, C., Li, H., Li, F. (2012). PCG: An Efficient Method for Composite Pattern Matching over Data Streams. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_41
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DOI: https://doi.org/10.1007/978-3-642-35527-1_41
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