Modular analysis of systems composed of semiautonomous subsystems

Lakos, Charles; Petrucci, Laure

doi:10.1109/csd.2004.1309131

Cited by 18 publications

(14 citation statements)

References 6 publications

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“…Several approaches have been proposed to distribute the verification of Petri net properties, e.g., by partitioning the state space using a hash function [14] or by modularizing the state space using localized strongly connected components [27]. These techniques do not consider event logs and cannot be applied to process mining.…”

Section: Related Workmentioning

confidence: 99%

Decomposing Process Mining Problems Using Passages

Aalst

2012

Lecture Notes in Computer Science

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Abstract. Process discovery-discovering a process model from example behavior recorded in an event log-is one of the most challenging tasks in process mining. Discovery approaches need to deal with competing quality criteria such as fitness, simplicity, precision, and generalization. Moreover, event logs may contain low frequent behavior and tend to be far from complete (i.e., typically only a fraction of the possible behavior is recorded). At the same time, models need to have formal semantics in order to reason about their quality. These complications explain why dozens of process discovery approaches have been proposed in recent years. Most of these approaches are time-consuming and/or produce poor quality models. In fact, simply checking the quality of a model is already computationally challenging. This paper shows that process mining problems can be decomposed into a set of smaller problems after determining the so-called causal structure. Given a causal structure, we partition the activities over a collection of passages. Conformance checking and discovery can be done per passage. The decomposition of the process mining problems has two advantages. First of all, the problem can be distributed over a network of computers. Second, due to the exponential nature of most process mining algorithms, decomposition can significantly reduce computation time (even on a single computer). As a result, conformance checking and process discovery can be done much more efficiently.

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Section: Related Workmentioning

confidence: 99%

Decomposing Process Mining Problems Using Passages

Aalst

2012

Lecture Notes in Computer Science

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“…Among them, onthe-fly model-checking (e.g., [10,5]) allows for generating only the "interesting" part of the model; partial order reduction (e.g., [1,19]) is a reduction technique exploiting independence of some transitions in the system to discard unnecessary parts; symbolic model-checking (e.g., [7,9,6]) aims at checking the property on a compact representation of the system by using BDD (Binary Decision Diagram) techniques [2]. More related to this paper, modular verification (e.g., [20,3,13,12]) is a promising natural approach which takes advantage of the modular design of concurrent and distributed systems. Using the "divide and conquer" principle, the system is broken down into components and each of these is analysed separately.…”

Section: Introductionmentioning

confidence: 99%

An Incremental and Modular Technique for Checking LTL∖X Properties of Petri Nets

Klaï

Petrucci

Reniers

2007

Lecture Notes in Computer Science

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Abstract. Model-checking is a powerful and widespread technique for the verification of finite state concurrent systems. However, the main hindrance for wider application of this technique is the well-known state explosion problem. Modular verification is a promising natural approach to tackle this problem. It is based on the "divide and conquer" principle and aims at deducing the properties of the system from those of its components analysed in isolation. Unfortunately, several issues make the use of modular verification techniques difficult in practice. First, deciding how to partition the system into components is not trivial and can have a significant impact on the resources needed for verification. Second, when model-checking a component in isolation, how should the environment of this component be described? In this paper, we address these problems in the framework of model-checking LTL\X action-based properties on Petri nets. We propose an incremental and modular verification approach where the system model is partitioned according to the actions occurring in the property to be verified and where the environment of a component is taken into account using the linear place invariants of the system.

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“…[14]) is an efficient method of decomposition based on the design of the system. Firstly, the state space is decomposed into two parts: sub-systems and a graph to synchronize these subsystems.…”

Section: Reduction Of the Model Sizementioning

confidence: 99%

State Space Reduction on Wireless Sensor Network Verification Using Component-Based Petri Net Approach

Bui

Tho

2016

REV-JEC

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Abstract-With the recent advancement of Internet of Things, the applications of Wireless Sensor Networks (WSNs) are increasingly attracting attention from of both industry and research communities. However, since the deployment cost of a WSN is relatively large, one would want to make a logic model of a WSN and have the model verified beforehand to ensure that the WSN would work correctly and effectively once practically employed. Petri Net (PN) is very suitable to model a WSN, since PN strongly supports modeling concurrent and ad-hoc systems. However, verification of a PN-modeled system suffers from having to explore the huge state space of the system. In order to overcome it, in this paper we suggest a novel component-based approach to model and verify a PN-modeled WSN system. First of all, the original WSN system is divided into components, which can be further abstracted to reduce the model size. Moreover, when verifying the corresponding PN model produced from the abstracted WSN, we introduce a strategy of component-based firing, which can reduce the state space significantly. Compared to typical approach of PN-based verification, our method enjoys an impressive improvement of performance and resource consuming, as depicted in our experimental results.

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Modular analysis of systems composed of semiautonomous subsystems

Cited by 18 publications

References 6 publications

Decomposing Process Mining Problems Using Passages

Decomposing Process Mining Problems Using Passages

An Incremental and Modular Technique for Checking LTL∖X Properties of Petri Nets

State Space Reduction on Wireless Sensor Network Verification Using Component-Based Petri Net Approach

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