{"title":"A technique for discovering BPMN collaboration diagrams","authors":"","doi":"10.1007/s10270-024-01153-5","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>The process mining domain is actively supported by techniques and tools addressing the discovery of single-participant business processes. In contrast, approaches for discovering collaboration models out of distributed data stored by multiple interacting participants are lacking. In this context, we propose a novel technique for discovering collaboration models from sets of event logs that include data about participants’ interactions. The technique discovers each participant’s process through already available algorithms introduced by the process mining community. Then, it analyzes the logs to extract information on the exchange of messages to automatically combine the discovered processes into a collaboration model representing the distributed system’s behavior and providing analytics on the interactions. The technique has been implemented in a tool evaluated via several experiments on different application domains.</p>","PeriodicalId":49507,"journal":{"name":"Software and Systems Modeling","volume":"68 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Software and Systems Modeling","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10270-024-01153-5","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
The process mining domain is actively supported by techniques and tools addressing the discovery of single-participant business processes. In contrast, approaches for discovering collaboration models out of distributed data stored by multiple interacting participants are lacking. In this context, we propose a novel technique for discovering collaboration models from sets of event logs that include data about participants’ interactions. The technique discovers each participant’s process through already available algorithms introduced by the process mining community. Then, it analyzes the logs to extract information on the exchange of messages to automatically combine the discovered processes into a collaboration model representing the distributed system’s behavior and providing analytics on the interactions. The technique has been implemented in a tool evaluated via several experiments on different application domains.
期刊介绍：
We invite authors to submit papers that discuss and analyze research challenges and experiences pertaining to software and system modeling languages, techniques, tools, practices and other facets. The following are some of the topic areas that are of special interest, but the journal publishes on a wide range of software and systems modeling concerns:
Domain-specific models and modeling standards;
Model-based testing techniques;
Model-based simulation techniques;
Formal syntax and semantics of modeling languages such as the UML;
Rigorous model-based analysis;
Model composition, refinement and transformation;
Software Language Engineering;
Modeling Languages in Science and Engineering;
Language Adaptation and Composition;
Metamodeling techniques;
Measuring quality of models and languages;
Ontological approaches to model engineering;
Generating test and code artifacts from models;
Model synthesis;
Methodology;
Model development tool environments;
Modeling Cyberphysical Systems;
Data intensive modeling;
Derivation of explicit models from data;
Case studies and experience reports with significant modeling lessons learned;
Comparative analyses of modeling languages and techniques;
Scientific assessment of modeling practices
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