{"title":"A Toolkit for Streaming Process Data Analysis","authors":"R. Dijkman, Sander P. F. Peters, A. T. Hofstede","doi":"10.1109/EDOCW.2016.7584341","DOIUrl":null,"url":null,"abstract":"This paper presents a software toolkit that can be used to analyze event data streams in real-time. It has a specific focus on stochastic analysis of business processes, based on event data that is produced during the execution of those processes. The toolkit provides a software environment that facilitates easy connection to event data streams and quick development and testing of analysis and visualization techniques. It is developed by classifying existing techniques for streaming process data analysis, which are identified in the current literature, and by extracting and formalizing the core mechanisms that these techniques are based on. These core mechanisms serve as the basis for the toolkit. The toolkit is implemented and made available as open source. In this way it can facilitate quick prototyping of streaming process data analysis techniques.","PeriodicalId":287808,"journal":{"name":"2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EDOCW.2016.7584341","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
This paper presents a software toolkit that can be used to analyze event data streams in real-time. It has a specific focus on stochastic analysis of business processes, based on event data that is produced during the execution of those processes. The toolkit provides a software environment that facilitates easy connection to event data streams and quick development and testing of analysis and visualization techniques. It is developed by classifying existing techniques for streaming process data analysis, which are identified in the current literature, and by extracting and formalizing the core mechanisms that these techniques are based on. These core mechanisms serve as the basis for the toolkit. The toolkit is implemented and made available as open source. In this way it can facilitate quick prototyping of streaming process data analysis techniques.