Abstract
Recently a growing number of applications monitor the physical world by detecting some patterns and trends of interest. In this paper we present two algorithms that generalize string-matching algorithms for detecting patterns with complex predicates over data streams having multiple categorical and quantitative attributes. Implementation and evaluation of the algorithms show their efficiency when compared to the naive approach.
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References
Babu, S., Widom, J.: Continuous Queries over Data Streams. SIGMOD Record, Vol. 30, No. 3 (2001) 109–120
Bonnet, P., Gehrke J., Seshadri P.: Towards Sensor Database Systems. Mobile Data Management 2001 (2001) 3–14
Gusfield, D.: Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology, Cambridge University Press (1997)
Harada, L.: Pattern Matching over Multi-Attribute Data Streams. Fujitsu Laboratories LTM2002-6843-302 (2002)
Sadri, R., Zaniolo, C, Zarkesh, A., Adibi, J.: Optimization of Sequence Queries in Database Systems. PODS2001 (2001) 71–81
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© 2002 Springer-Verlag Berlin Heidelberg
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Harada, L. (2002). Pattern Matching over Multi-attribute Data Streams. In: Laender, A.H.F., Oliveira, A.L. (eds) String Processing and Information Retrieval. SPIRE 2002. Lecture Notes in Computer Science, vol 2476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45735-6_17
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DOI: https://doi.org/10.1007/3-540-45735-6_17
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