使用整数线性规划的进程发现

IF 0.4 4区计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Fundamenta Informaticae Pub Date : 2009-08-01 DOI:10.3233/FI-2009-136

J. V. D. Werf, B. V. Dongen, C. Hurkens, Alexander Serebrenik

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引用次数: 395

摘要

过程发现的研究领域旨在构建过程模型(如Petri网)，该模型是执行日志的抽象表示。这样的模型应该(1)能够再现所考虑的日志，(2)独立于日志中的案例数量。在本文中，我们提出了一种过程发现算法，其中我们使用了基于语言的区域理论的概念，这是一个著名的Petri网研究领域。我们从过程发现的角度确定了该理论的一些缺点，并提供了基于整数线性规划的解决方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Process Discovery using Integer Linear Programming

The research domain of process discovery aims at constructing a process model (e.g. a Petri net) which is an abstract representation of an execution log. Such a model should (1) be able to reproduce the log under consideration and (2) be independent of the number of cases in the log. In this paper, we present a process discovery algorithm where we use concepts taken from the language-based theory of regions, a well-known Petri net research area. We identify a number of shortcomings of this theory from the process discovery perspective, and we provide solutions based on integer linear programming.

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来源期刊

Fundamenta Informaticae 工程技术-计算机：软件工程

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

9.8 months

期刊介绍： Fundamenta Informaticae is an international journal publishing original research results in all areas of theoretical computer science. Papers are encouraged contributing: solutions by mathematical methods of problems emerging in computer science solutions of mathematical problems inspired by computer science. Topics of interest include (but are not restricted to): theory of computing, complexity theory, algorithms and data structures, computational aspects of combinatorics and graph theory, programming language theory, theoretical aspects of programming languages, computer-aided verification, computer science logic, database theory, logic programming, automated deduction, formal languages and automata theory, concurrency and distributed computing, cryptography and security, theoretical issues in artificial intelligence, machine learning, pattern recognition, algorithmic game theory, bioinformatics and computational biology, quantum computing, probabilistic methods, algebraic and categorical methods.