{"title":"MantaRay-ProM:一种高效的流程模型发现算法","authors":"Shikha Gupta, Sonia Deshmukh, Naveen Kumar","doi":"10.3233/aic-220219","DOIUrl":null,"url":null,"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.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"41 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MantaRay-ProM: An efficient process model discovery algorithm\",\"authors\":\"Shikha Gupta, Sonia Deshmukh, Naveen Kumar\",\"doi\":\"10.3233/aic-220219\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":50835,\"journal\":{\"name\":\"AI Communications\",\"volume\":\"41 1\",\"pages\":\"\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2024-04-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AI Communications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.3233/aic-220219\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AI Communications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.3233/aic-220219","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
MantaRay-ProM: An efficient process model discovery algorithm
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.
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.