Pub Date : 2022-06-14DOI: 10.48550/arXiv.2206.07080
Carl Corea, J. Grant, Matthias Thimm
. We address the problem of measuring inconsistency in declarative process specifications, with an emphasis on linear temporal logic on fixed traces (LTL ff ). As we will show, existing inconsistency measures for classical logic cannot provide a meaningful assessment of inconsistency in LTL in general, as they cannot adequately handle the temporal operators. We therefore propose a novel paraconsistent semantics as a framework for inconsistency measurement. We then present two new inconsistency measures based on these semantics and show that they satisfy important desirable properties. We show how these measures can be applied to declarative process models and investigate the computational complexity of the introduced approach.
{"title":"Measuring Inconsistency in Declarative Process Specifications","authors":"Carl Corea, J. Grant, Matthias Thimm","doi":"10.48550/arXiv.2206.07080","DOIUrl":"https://doi.org/10.48550/arXiv.2206.07080","url":null,"abstract":". We address the problem of measuring inconsistency in declarative process specifications, with an emphasis on linear temporal logic on fixed traces (LTL ff ). As we will show, existing inconsistency measures for classical logic cannot provide a meaningful assessment of inconsistency in LTL in general, as they cannot adequately handle the temporal operators. We therefore propose a novel paraconsistent semantics as a framework for inconsistency measurement. We then present two new inconsistency measures based on these semantics and show that they satisfy important desirable properties. We show how these measures can be applied to declarative process models and investigate the computational complexity of the introduced approach.","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130093496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-11DOI: 10.1007/978-3-031-16171-1_12
Gyunam Park, Janik-Vasily Benzin, Wil M.P. van der Aalst
{"title":"Detecting Context-Aware Deviations in Process Executions","authors":"Gyunam Park, Janik-Vasily Benzin, Wil M.P. van der Aalst","doi":"10.1007/978-3-031-16171-1_12","DOIUrl":"https://doi.org/10.1007/978-3-031-16171-1_12","url":null,"abstract":"","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130809395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-07DOI: 10.1007/978-3-031-16168-1_1
Fabian Stiehle, I. Weber
{"title":"Blockchain for Business Process Enactment: A Taxonomy and Systematic Literature Review","authors":"Fabian Stiehle, I. Weber","doi":"10.1007/978-3-031-16168-1_1","DOIUrl":"https://doi.org/10.1007/978-3-031-16168-1_1","url":null,"abstract":"","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133682929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-08DOI: 10.48550/arXiv.2204.04135
Marco Pegoraro, M. S. Uysal, Wil M.P. van der Aalst
Event data, often stored in the form of event logs, serve as the starting point for process mining and other evidence-based process improvements. However, event data in logs are often tainted by noise, errors, and missing data. Recently, a novel body of research has emerged, with the aim to address and analyze a class of anomalies known as uncertainty-imprecisions quantified with meta-information in the event log. This paper illustrates an extension of the XES data standard capable of representing uncertain event data. Such an extension enables input, output, and manipulation of uncertain data, as well as analysis through the process discovery and conformance checking approaches available in literature.
{"title":"An XES Extension for Uncertain Event Data","authors":"Marco Pegoraro, M. S. Uysal, Wil M.P. van der Aalst","doi":"10.48550/arXiv.2204.04135","DOIUrl":"https://doi.org/10.48550/arXiv.2204.04135","url":null,"abstract":"Event data, often stored in the form of event logs, serve as the starting point for process mining and other evidence-based process improvements. However, event data in logs are often tainted by noise, errors, and missing data. Recently, a novel body of research has emerged, with the aim to address and analyze a class of anomalies known as uncertainty-imprecisions quantified with meta-information in the event log. This paper illustrates an extension of the XES data standard capable of representing uncertain event data. Such an extension enables input, output, and manipulation of uncertain data, as well as analysis through the process discovery and conformance checking approaches available in literature.","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129373407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-29DOI: 10.48550/arXiv.2203.15398
Stefano Branchi, Chiara Di Francescomarino, Chiara Ghidini, David Massimo, Francesco Ricci, Massimiliano Ronzani
. The rise of process data availability has led in the last decade to the development of several data-driven learning approaches. However, most of these approaches limit themselves to use the learned model to predict the future of ongoing process executions. The goal of this paper is moving a step forward and leveraging data with the purpose of learning to act by supporting users with recommendations for the best strategy to follow, in order to optimize a measure of performance. In this paper, we take the (optimization) perspective of one process actor and we recommend the best activities to execute next, in response to what happens in a complex external environment, where there is no control on exogenous factors. To this aim, we investigate an approach that learns, by means of Reinforcement Learning, an optimal policy from the observation of past executions and recommends the best activities to carry on for optimizing a Key Performance Indicator of interest. The potentiality of the approach has been demonstrated on two scenarios taken from real-life data.
{"title":"Learning to act: a Reinforcement Learning approach to recommend the best next activities","authors":"Stefano Branchi, Chiara Di Francescomarino, Chiara Ghidini, David Massimo, Francesco Ricci, Massimiliano Ronzani","doi":"10.48550/arXiv.2203.15398","DOIUrl":"https://doi.org/10.48550/arXiv.2203.15398","url":null,"abstract":". The rise of process data availability has led in the last decade to the development of several data-driven learning approaches. However, most of these approaches limit themselves to use the learned model to predict the future of ongoing process executions. The goal of this paper is moving a step forward and leveraging data with the purpose of learning to act by supporting users with recommendations for the best strategy to follow, in order to optimize a measure of performance. In this paper, we take the (optimization) perspective of one process actor and we recommend the best activities to execute next, in response to what happens in a complex external environment, where there is no control on exogenous factors. To this aim, we investigate an approach that learns, by means of Reinforcement Learning, an optimal policy from the observation of past executions and recommends the best activities to carry on for optimizing a Key Performance Indicator of interest. The potentiality of the approach has been demonstrated on two scenarios taken from real-life data.","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114125531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-14DOI: 10.48550/arXiv.2203.07507
Eli Bogdanov, Izack Cohen, A. Gal
With the growing number of devices, sensors and digital systems, data logs may become uncertain due to, e.g., sensor reading inaccuracies or incorrect interpretation of readings by processing programs. At times, such uncertainties can be captured stochastically, especially when using probabilistic data classification models. In this work we focus on conformance checking, which compares a process model with an event log, when event logs are stochastically known. Building on existing alignment-based conformance checking fundamentals, we mathematically define a stochastic trace model, a stochastic synchronous product, and a cost function that reflects the uncertainty of events in a log. Then, we search for an optimal alignment over the reachability graph of the stochastic synchronous product for finding an optimal alignment between a model and a stochastic process observation. Via structured experiments with two well-known process mining benchmarks, we explore the behavior of the suggested stochastic conformance checking approach and compare it to a standard alignment-based approach as well as to an approach that creates a lower bound on performance. We envision the proposed stochastic conformance checking approach as a viable process mining component for future analysis of stochastic event logs.
{"title":"Conformance Checking Over Stochastically Known Logs","authors":"Eli Bogdanov, Izack Cohen, A. Gal","doi":"10.48550/arXiv.2203.07507","DOIUrl":"https://doi.org/10.48550/arXiv.2203.07507","url":null,"abstract":"With the growing number of devices, sensors and digital systems, data logs may become uncertain due to, e.g., sensor reading inaccuracies or incorrect interpretation of readings by processing programs. At times, such uncertainties can be captured stochastically, especially when using probabilistic data classification models. In this work we focus on conformance checking, which compares a process model with an event log, when event logs are stochastically known. Building on existing alignment-based conformance checking fundamentals, we mathematically define a stochastic trace model, a stochastic synchronous product, and a cost function that reflects the uncertainty of events in a log. Then, we search for an optimal alignment over the reachability graph of the stochastic synchronous product for finding an optimal alignment between a model and a stochastic process observation. Via structured experiments with two well-known process mining benchmarks, we explore the behavior of the suggested stochastic conformance checking approach and compare it to a standard alignment-based approach as well as to an approach that creates a lower bound on performance. We envision the proposed stochastic conformance checking approach as a viable process mining component for future analysis of stochastic event logs.","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130708865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-02DOI: 10.1007/978-3-031-16171-1_6
P. Fettke, W. Reisig
{"title":"Systems Mining with Heraklit: The Next Step","authors":"P. Fettke, W. Reisig","doi":"10.1007/978-3-031-16171-1_6","DOIUrl":"https://doi.org/10.1007/978-3-031-16171-1_6","url":null,"abstract":"","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114907373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-24DOI: 10.1007/978-3-030-85440-9_2
M. Goldstein, Cecilia González-Alvarez
{"title":"Augmenting Modelers with Semantic Autocompletion of Processes","authors":"M. Goldstein, Cecilia González-Alvarez","doi":"10.1007/978-3-030-85440-9_2","DOIUrl":"https://doi.org/10.1007/978-3-030-85440-9_2","url":null,"abstract":"","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127686251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-19DOI: 10.1007/978-3-030-85469-0_24
Yang Lu, Qifan Chen, Simon K. Poon
{"title":"A Robust and Accurate Approach to Detect Process Drifts from Event Streams","authors":"Yang Lu, Qifan Chen, Simon K. Poon","doi":"10.1007/978-3-030-85469-0_24","DOIUrl":"https://doi.org/10.1007/978-3-030-85469-0_24","url":null,"abstract":"","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122160598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-18DOI: 10.1007/978-3-030-85469-0_15
Paolo Felli, Alessandro Gianola, M. Montali, Andrey Rivkin, S. Winkler
{"title":"CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT (Extended Version)","authors":"Paolo Felli, Alessandro Gianola, M. Montali, Andrey Rivkin, S. Winkler","doi":"10.1007/978-3-030-85469-0_15","DOIUrl":"https://doi.org/10.1007/978-3-030-85469-0_15","url":null,"abstract":"","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125983744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：