MantaRay-ProM: An efficient process model discovery algorithm

IF 1.4 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shikha Gupta, Sonia Deshmukh, Naveen Kumar
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引用次数: 0

Abstract

Discovering the business process model from an organisation’s records of its operational processes is an active area of research in process mining. The discovered model may be used either during a new system rollout or to improve an existing system. In this paper, we present a process model discovery approach based on the recently proposed bio-inspired Manta Ray Foraging Optimization algorithm (MRFO). Since MRFO is designed to solve real-valued optimization problems, we adapted a binary version of MRFO to suit the domain of process mining. The proposed approach is compared with state-of-the-art process discovery algorithms on several synthetic and real-life event logs. The results show that compared to other algorithms, the proposed approach exhibits faster convergence and yields superior quality process models.
MantaRay-ProM:一种高效的流程模型发现算法
从企业的业务流程记录中发现业务流程模型是流程挖掘的一个活跃研究领域。发现的模型既可用于新系统的推广,也可用于改进现有系统。在本文中,我们介绍了一种基于最近提出的生物启发蝠鲼觅食优化算法(MRFO)的流程模型发现方法。由于 MRFO 是为解决实值优化问题而设计的,我们对二进制版本的 MRFO 进行了调整,以适应流程挖掘领域。我们在多个合成和真实事件日志上将所提出的方法与最先进的流程发现算法进行了比较。结果表明,与其他算法相比,所提出的方法收敛速度更快,所生成的流程模型质量更高。
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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来源期刊
AI Communications
AI Communications 工程技术-计算机:人工智能
CiteScore
2.30
自引率
12.50%
发文量
34
审稿时长
4.5 months
期刊介绍: AI Communications is a journal on artificial intelligence (AI) which has a close relationship to EurAI (European Association for Artificial Intelligence, formerly ECCAI). It covers the whole AI community: Scientific institutions as well as commercial and industrial companies. AI Communications aims to enhance contacts and information exchange between AI researchers and developers, and to provide supranational information to those concerned with AI and advanced information processing. AI Communications publishes refereed articles concerning scientific and technical AI procedures, provided they are of sufficient interest to a large readership of both scientific and practical background. In addition it contains high-level background material, both at the technical level as well as the level of opinions, policies and news.
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