A fast string searching algorithm
Communications of the ACM
Efficient string matching: an aid to bibliographic search
Communications of the ACM
Optimization of sequence queries in database systems
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Flexible pattern matching in strings: practical on-line search algorithms for texts and biological sequences
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Complex Temporal Patterns Detection over Continuous Data Streams
ADBIS '02 Proceedings of the 6th East European Conference on Advances in Databases and Information Systems
Pattern Matching over Multi-attribute Data Streams
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
"One Size Fits All": An Idea Whose Time Has Come and Gone
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
SWOD '07 Proceedings of the 2007 IEEE International Workshop on Databases for Next Generation Researchers
On bit-parallel processing of multi-byte text
AIRS'04 Proceedings of the 2004 international conference on Asian Information Retrieval Technology
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In this paper, we study a complex time-series pattern matching problem over a multi-dimension continuous data stream. For each data stream, a pattern is given as a sequence of predicates, which specify a sequence of element sets on the stream. The pattern matching problem over such a multi-dimension data stream, is to find all occurrences where all predicates in the patterns are satisfied. We propose a flexible and extensible framework to solve the problem, which is based on bit-parallel pattern matching method that simulates NFAs for the pattern matching efficiently by a few logical bit operations. We consider four types of data streams especially: textual, categorical, ordered, and numeric, that is, those are a sequence of strings, concepts with taxonomic information, small integers, and real numbers (or large integers), respectively. We also present the time complexities to do pattern matching for those data types.