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Extended Timed Regular Expressions
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AI
DOI：10.1007/978-3-032-05435-7_14.png)
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Abstract
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The behavior of complex organisms or systems is often stored as time series data. Time series data is valuable because it contains valuable information in the form of timed patterns. However, this patterns are difficult to formalize and to detect. We present Extended Timed Regular Expressions (ETRE) to express complex timed patterns which can be systematically and efficiently matched in large sets of time series data. We translate ETRE to Timed Automata (TA), where pattern matching is computed by reachability analysis in TA. We implement our theory using C++ in the new tool TimeRex. Our tool can be used for online (run time) or offline (post processing) pattern matching. We run extensive experiments on real data. We have been able to efficiently match a number of relevant patterns.
Keywords:
Extended Timed Regular Expressions
Time Series Data
Timed Automata
Pattern Matching
Reachability Analysis
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