Timed Pattern Matching Research Articles

Elements of Timed Pattern Matching

The rise of machine learning and cloud technologies has led to a remarkable influx of data within modern cyber-physical systems. However, extracting meaningful information from this data has become a significant challenge due to its volume and complexity. Timed pattern matching has emerged as a powerful specification-based runtime verification and temporal data analysis technique to address this challenge. In this paper, we provide a comprehensive tutorial on timed pattern matching that ranges from the underlying algebra and pattern specification languages to performance analyses and practical case studies. Analogous to textual pattern matching, timed pattern matching is the task of finding all time periods within temporal behaviors of cyber-physical systems that match a predefined pattern. Originally we introduced and solved several variants of the problem using the name of match sets, which has evolved into the concept of timed relations over the past decade. Here we first formalize and present the algebra of timed relations as a standalone mathematical tool to solve the pattern matching problem of timed pattern specifications. In particular, we show how to use the algebra of timed relations to solve the pattern matching problem for timed regular expressions and metric compass logic in a unified manner. We experimentally demonstrate that our timed pattern matching approach performs and scales well in practice. We further provide in-depth insights into the similarities and fundamental differences between monitoring and matching problems as well as regular expressions and temporal logic formulas. Finally, we illustrate the practical application of timed pattern matching through two case studies, which show how to extract structured information from temporal datasets obtained via simulations or real-world observations. These results and examples show that timed pattern matching is a rigorous and efficient technique in developing and analyzing cyber-physical systems.

Parametric 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 real-time systems. We address here timed pattern matching in the presence of an uncertain specification, i.e., that may contain timing parameters (e.g., the deadline can be uncertain or unknown). We want to know for which start and end dates, and for what values of the timing parameters, a property holds. For instance, we look for the minimum or maximum deadline (together with the corresponding start and end dates) for which the property holds. We propose two frameworks for parametric timed pattern matching. The first one is based on parametric timed model checking. In contrast to most parametric timed problems, the solution is effectively computable. The second one is a dedicated method; not only we largely improve the efficiency compared to the first method, but we further propose optimizations with skipping. Our experiment results suggest that our algorithms, especially the second one, are efficient and practically relevant.

Moore-Machine Filtering for Timed and Untimed Pattern Matching

Monitoring is an important body of techniques in runtime verification of real-time, embedded, and cyber-physical systems. Mathematically, the monitoring problem can be formalized as a pattern matching problem against a pattern automaton. Motivated by the needs in embedded applications---especially the limited channel capacity between a sensor unit and a processor that monitors---we pursue the idea of filtering as preprocessing for monitoring. Technically, for a given pattern automaton, we present a construction of a Moore machine that works as a filter. The construction is automata-theoretic, and we find the use of Moore machines particularly suited for embedded applications, not only because their sequential operation is relatively cheap but also because they are amenable to hardware acceleration by dedicated circuits. We prove soundness (i.e., absence of lost matches), too. We work in two settings: in the untimed one, a pattern is an NFA; in the timed one, a pattern is a timed automaton. The extension of our untimed construction to the timed setting is technically involved, but our experiments demonstrate its practical benefits.

Mechanical Diagnosis Based on Similarity Extraction of Time Series

The intelligent diagnosis emphasizes the processing method of knowledge of the historical data. The capability of an intelligent diagnosis system depends on the knowledge possessed by the system, especially by the specific knowledge in application. Currently, most of the important equipment have their own the inspection systems. With the help of these systems, plenty of historical data can be collected in real time. This paper discusses the possibility of the application of similarity extraction and pattern discovery of time series in fault diagnosis by using these historical data, presents the method of time series feature extraction and pattern matching, and advances the possibility of data clustering and pattern discovery based on dimension reduction.

Robust Real-Time Pattern Matching Using Bayesian Sequential Hypothesis Testing

This paper describes a method for robust real time pattern matching. We first introduce a family of image distance measures, the "Image Hamming Distance Family". Members of this family are robust to occlusion, small geometrical transforms, light changes and non-rigid deformations. We then present a novel Bayesian framework for sequential hypothesis testing on finite populations. Based on this framework, we design an optimal rejection/acceptance sampling algorithm. This algorithm quickly determines whether two images are similar with respect to a member of the Image Hamming Distance Family. We also present a fast framework that designs a near-optimal sampling algorithm. Extensive experimental results show that the sequential sampling algorithm performance is excellent. Implemented on a Pentium 4 3 GHz processor, detection of a pattern with 2197 pixels, in 640 x 480 pixel frames, where in each frame the pattern rotated and was highly occluded, proceeds at only 0.022 seconds per frame.

Effects of Auditory Distraction on Cognitive Processing of Young Adults

Objective: The effects of interference, competition, and distraction on cognitive processing are unclearly understood, particularly regarding type and intensity of auditory distraction across a variety of cognitive processing tasks. Method: The purpose of this investigation was to report two experiments that sought to explore the effects of types of distraction (4-talker babble; word repetition; combined 4-talker babble with word repetition) when compared to a control condition of quiet on a range of computerized measures (simple reaction time; choice reaction time; serial pattern matching; lexical decision-making; visual selective attention; response reversal and rapid visual scanning; and form discrimination) in 40 young adults (Experiment 1). Results: Few distraction effects were found on cognitive processing at the comfortable loudness level (40dB SL). In Experiment 2, statistically significant differences for choice reaction time and serial pattern matching (working memory) were found in both speed and accuracy when distractions were presented at perceived uncomfortable loudness levels (ULL) and compared with conditions of quiet and comfortable loudness level (40dB SL). Conclusion: Cognitive resource allocation models may be useful in interpreting the effects of auditory distraction on cognitive-linguistic processing, and this model may help to explain differential distraction effects in clinical populations with attention deficits. (J. of Att. Dis. 2007; 10(4) 398-409)

Neural networks in ocean engineering

The soft computing technique of neural network is being extensively used across all disciplines of ocean engineering, namely, offshore, coastal, and deep-ocean engineering including marine engineering. This paper takes a stock of the research studies reported so far in these areas. It is found that, in general, neural networks provide a better alternative, either substitutive or complementary, to traditional computational schemes of statistical regression, time series analysis, pattern matching, and numerical methods. The relative advantages of the neural network schemes proposed by various investigators are improved accuracy, lesser complexity in modeling and hence smaller computational effort and time, reduced data requirement in some cases, and so on. Neural networks have a very high degree of freedom, and that comes as handy while training it with examples. Exploration of more areas of application, implementation of advanced and hybrid forms of networks together with interpretation of the information contained in a trained network should receive more focus in future. Similarly the current difficulties in dealing with very large variations in the input, large warning times, extreme value predictions, and extrapolation beyond the observed range would have to be addressed in the near future.

Decision-tree programs for an adaptive automated chemistry workstation. Application to catalyst screening experiments

Automated chemistry workstations with the capability of altering the course of experimentation based on acquired data are essential for performing strategic searches. Such adaptive experimentation requires the ability to compose experimental plans for initiating, monitoring, evaluating, and making decisions about experiments. We have developed a decision-tree (DT) experiment-planning module that includes a complete command set for adaptive operation of the workstation. Functions for dialog prompts are included, enabling generic programs to be tailored for specific applications by various users. The DT experiment-planning module is one component of an experiment planner for an automated microscale chemistry workstation. The DT functions are also used to compose early-termination programs and describe objective functions for use in the companion factorial design, composite-modified simplex, and multidirectional search experiment-planning modules. The features of the DT experiment-planning module are illustrated with a program that screens a list of candidate compounds for catalytic activity. A set of dialog boxes at the beginning of the program prompts the user for information. Based on this information, the candidates are examined automatically (in a serial process) in exhaustive or hill-climbing searching strategies. A yield vs. time pattern-matching procedure is performed dynamically in order to assess whether monitoring should be continued. Decisions also are made concerning examination of the candidate at higher or lower concentrations. Higher concentrations are achieved by addition of aliquots of the candidate to the ongoing reaction, while lower concentrations are achieved by spawning a new reaction. When a user-defined yield threshold is reached or the entire user-specified concentration range has been spanned, catalysts and their effective concentration ranges are identified. This experiment-planning module enables composition of flexible programs for adaptive experimentation that can be used as stand-alone programs or as decision-making components of other searching programs.