231 research outputs found

    A Two Phase Verification Algorithm for Cyclic Workflow Graphs

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    The widespread automation of e-business processes has made workflow analysis and design an integral part of information management. Graph-based workflow models enables depicting complex processes in a fairly compact form. This free form, on the other hand, can yield models that may fail depending on the judgment of the modeler and create modeling situations that cannot be executed or will behave in a manner not expected by the modeler. Further, cycles in workflow models needed for purposes of rework and information feedback increase the complexity of workflow analysis. This paper presents a novel method of partitioning a cyclic workflow process, represented in a directed graph, into a set of acyclic subgraphs. This allows a cyclic workflow model to be analyzed further with several smaller subflows, which are all acyclic. As a convincing example, we present two-phased verification of structural conflicts in workflow models for those incurred from the inappropriate composition of partitioned flows and the others within each acyclic subgraph, which is much easier to comprehend and verify individually than the whole workflow model, in general

    A Formal Framework for Data-Aware Process Interaction Models

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    IT support for distributed and collaborative workflows as well as related interactions between business partners are becoming increasingly important. For modeling such partner interactions as flow of message exchanges, different topdown approaches, covered under the term interaction modeling, are provided. Like for workflow models, correctness constitutes a fundamental challenge for interaction models; e.g., to ensure the boundedness and absence of deadlocks and lifelocks. Due to their distributed execution, in addition, interaction models should be message-deterministic and realizable, i.e., the same conversation (i.e. sequence of messages) should always lead to the same result, and it should be ensured that partners always have enough information about the messages they must or may send in a given context. So far, most existing approaches have addressed correctness of interaction models without explicitly considering the data exchanged through messages and used for routing decisions. However, data support is crucial for collaborative workflows and interaction models respectively. This technical report enriches interaction models with the data perspective. In particular, it defines the behavior of data-aware interaction models based on Data- Aware Interaction Nets, which use elements of both Interaction Petri Nets and Workflow Nets with Data. Finally, formal correctness criteria for Data-Aware Interaction Nets are derived, guaranteeing the boundedness and absence of deadlocks and lifelocks, and ensuring message-determinism as well as realizability

    Obstructions in Security-Aware Business Processes

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    This Open Access book explores the dilemma-like stalemate between security and regulatory compliance in business processes on the one hand and business continuity and governance on the other. The growing number of regulations, e.g., on information security, data protection, or privacy, implemented in increasingly digitized businesses can have an obstructive effect on the automated execution of business processes. Such security-related obstructions can particularly occur when an access control-based implementation of regulations blocks the execution of business processes. By handling obstructions, security in business processes is supposed to be improved. For this, the book presents a framework that allows the comprehensive analysis, detection, and handling of obstructions in a security-sensitive way. Thereby, methods based on common organizational security policies, process models, and logs are proposed. The Petri net-based modeling and related semantic and language-based research, as well as the analysis of event data and machine learning methods finally lead to the development of algorithms and experiments that can detect and resolve obstructions and are reproducible with the provided software

    Computer Aided Verification

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    This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book

    Tools and Algorithms for the Construction and Analysis of Systems

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    This book is Open Access under a CC BY licence. The LNCS 11427 and 11428 proceedings set constitutes the proceedings of the 25th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2019, which took place in Prague, Czech Republic, in April 2019, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019. The total of 42 full and 8 short tool demo papers presented in these volumes was carefully reviewed and selected from 164 submissions. The papers are organized in topical sections as follows: Part I: SAT and SMT, SAT solving and theorem proving; verification and analysis; model checking; tool demo; and machine learning. Part II: concurrent and distributed systems; monitoring and runtime verification; hybrid and stochastic systems; synthesis; symbolic verification; and safety and fault-tolerant systems

    A Framework for the Objective Assessment of Registration Accuracy

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    Validation and accuracy assessment are themain bottlenecks preventing the adoption of image processing algorithms in the clinical practice. In the classical approach, a posteriori analysis is performed through objective metrics. In this work, a different approach based on Petri nets is proposed.The basic idea consists in predicting the accuracy of a given pipeline based on the identification and characterization of the sources of inaccuracy. The concept is demonstrated on a case study: the intrasubject rigid and affine registration of magnetic resonance images. A choice of possible sources of inaccuracies that can affect the registration process is accounted for, and an estimation of the overall inaccuracy is provided through Petri nets. Both synthetic and real data are considered. While synthetic data allow the benchmarking of the performance with respect to the ground truth, real data enable to assess the robustness of the methodology in real contexts as well as to determine the suitability of the use of synthetic data in the training phase. Results revealed a higher correlation and a lower dispersion among the metrics for simulated data, while the opposite trend was observed for pathologic ones. Results show that the proposedmodel not only provides a good prediction performance but also leads to the optimization of the end-to-end chain in terms of accuracy and robustness, setting the ground for its generalization to different and more complex scenarios

    Aligning observed and modeled behavior

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