Abstract. The timed pattern matching problem is formulated by Ulus et al. and has been actively studied since, with its evident application in monitoring realtime systems. The problem takes as input a timed word/signal and a timed pattern (specified either by a timed regular expression or by a timed automaton); and it returns the set of those intervals for which the given timed word, when restricted to the interval, matches the given pattern. We contribute a Boyer-Moore type optimization in timed pattern matching, relying on the classic Boyer-Moore string matching algorithm and its extension to (untimed) pattern matching by Watson and Watson. We assess its effect through experiments; for some problem instances our Boyer-Moore type optimization achieves speed-up by two times, indicating its potential in real-world monitoring tasks where data sets tend to be massive.
Abstract. The timed pattern matching problem is an actively studied topic because of its relevance in monitoring of real-time systems. There one is given a log w and a specification A (given by a timed word and a timed automaton in this paper), and one wishes to return the set of intervals for which the log w, when restricted to the interval, satisfies the specification A. In our previous work we presented an efficient timed pattern matching algorithm: it adopts a skipping mechanism inspired by the classic Boyer-Moore (BM) string matching algorithm. In this work we tackle the problem of online timed pattern matching, towards embedded applications where it is vital to process a vast amount of incoming data in a timely manner. Specifically, we start with the Franek-Jennings-Smyth (FJS) string matching algorithm-a recent variant of the BM algorithm-and extend it to timed pattern matching. Our experiments indicate the efficiency of our FJStype algorithm in online and offline timed pattern matching.
Given a log and a specification, timed pattern matching aims at exhibiting for which start and end dates a specification holds on that log. For example, "a given action is always followed by another action before a given deadline". This problem has strong connections with monitoring realtime systems. We address here timed pattern matching in presence of an uncertain specification, i. e., that may contain timing parameters (e. g., the deadline can be uncertain or unknown). That is, we want to know for which start and end dates, and for what values of the deadline, this property holds. Or what is the minimum or maximum deadline (together with the corresponding start and end dates) for which this property holds. We propose here a framework for timed pattern matching based on parametric timed model checking. In contrast to most parametric timed problems, the solution is effectively computable, and we perform experiments using IMITATOR to show the applicability of our approach.
We present a method to extract a weighted finite automaton (WFA) from a recurrent neural network (RNN). Our method is based on the WFA learning algorithm by Balle and Mohri, which is in turn an extension of Angluin's classic L* algorithm. Our technical novelty is in the use of regression methods for the so-called equivalence queries, thus exploiting the internal state space of an RNN to prioritize counterexample candidates. This way we achieve a quantitative/weighted extension of the recent work by Weiss, Goldberg and Yahav that extracts DFAs. We experimentally evaluate the accuracy, expressivity and efficiency of the extracted WFAs.
This report presents the results from the 2021 friendly competition in the ARCH work- shop for the falsification of temporal logic specifications over Cyber-Physical Systems. We briefly describe the competition settings, which have been inherited from the previ- ous years, give background on the participating teams and tools and discuss the selected benchmarks. Apart from new requirements and participants, the major novelty in this instalment is that falsifying inputs have been validated independently. During this pro- cess, we uncovered several issues like configuration errors and computational discrepancies, stressing the importance of this kind of validation.
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