不打结的健全性:通过松散循环对任意循环过程模型进行高效健全性检查的算法

IF 3 2区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Thomas M. Prinz , Yongsun Choi , N. Long Ha
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引用次数: 0

摘要

尽管领域专家通常会创建业务流程模型,但这些模型仍可能包含错误。因此,研究和实践为流程模型建立了标准,以提供对流程正确性或正确行为的信心。其中一个广泛使用的标准是健全性,它能保证没有死锁和缺乏同步。检查流程模型的健全性并非易事。然而,循环流程模型会额外增加检查合理性的复杂性。本文提出了一种验证健全性的新方法,这种方法具有高效的立方最坏运行时行为,即使对于任意循环流程模型也是如此。这种方法依靠三种关键技术--循环转换、循环缩减和循环分解--将任意循环流程模型转换为一组非循环流程模型。利用这种方法,我们开发了五种简单明了的规则来验证其合理性,并重复使用现有的方法来检查非循环模型的合理性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Soundness unknotted: An efficient soundness checking algorithm for arbitrary cyclic process models by loosening loops
Although domain experts usually create business process models, these models can still contain errors. For this reason, research and practice establish criteria for process models to provide confidence in the correctness or correct behavior of processes. One widespread criterion is soundness, which guarantees the absence of deadlocks and lacks of synchronization. Checking soundness of process models is not trivial. However, cyclic process models additionally increase the complexity to check soundness. This paper presents a novel approach for verifying soundness that has an efficient cubic worst-case runtime behavior, even for arbitrary cyclic process models. This approach relies on three key techniques — loop conversion, loop reduction, and loop decomposition — to convert any cyclic process model into a set of acyclic process models. Using this approach, we have developed five straightforward rules to verify the soundness, reusing existing approaches for checking soundness of acyclic models.
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来源期刊
Information Systems
Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
2.70%
发文量
112
审稿时长
53 days
期刊介绍: Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.
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