学习工作流程Petri网

IF 0.4 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Fundamenta Informaticae Pub Date : 2010-06-21 DOI:10.3233/FI-2011-607
J. Esparza, M. Leucker, M. Schlund
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引用次数: 30

摘要

工作流挖掘是从一组记录工作流事件序列的事件日志中自动生成工作流模型的任务;每个序列对应于一个用例或工作流实例。工作流挖掘的正式方法假设事件日志是完整的(包含足够的信息来推断工作流),但通常情况并非如此。我们提出了一种学习方法来放松这种假设:如果事件日志是不完整的,我们的学习算法会自动派生关于某些事件序列的可执行性的查询。如果老师回答了这些问题,那么算法就可以保证以正确的模型结束。我们提供了算法所需查询数量的匹配上限和下限,并报告了一些示例的实现应用。
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Learning Workflow Petri Nets
Workflow mining is the task of automatically producing a workflow model from a set of event logs recording sequences of workflow events; each sequence corresponds to a use case or workflow instance. Formal approaches to workflow mining assume that the event log is complete (contains enough information to infer the workflow) which is often not the case. We present a learning approach that relaxes this assumption: if the event log is incomplete, our learning algorithm automatically derives queries about the executability of some event sequences. If a teacher answers these queries, the algorithm is guaranteed to terminate with a correct model. We provide matching upper and lower bounds on the number of queries required by the algorithm, and report on the application of an implementation to some examples.
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来源期刊
Fundamenta Informaticae
Fundamenta Informaticae 工程技术-计算机:软件工程
CiteScore
2.00
自引率
0.00%
发文量
61
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.
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