Pub Date : 2021-01-14DOI: 10.15495/EPUB_UBT_00005329
Wolfgang Kratsch
Business process management (BPM) is an accepted paradigm of organizational design and a source of corporate performance [1]. Due to substantial progress in process identification, analysis, implementation, and improvement [2, 3], BPM receives constant attention from industry [4]. In times of market consolidation and increasing competition, operational excellence (i.e., continuously optimizing an organization’s processes in terms of effectiveness and efficiency) is key to staying competitive. While traditional research in BPM focused on process models and model-based information systems (e.g., workflow management systems), recently, the focus has shifted to datadriven methods such as process mining [5]. In contrast to model-driven BPM, process mining uses execution data in the form of events arising during process enactment, which may be exploited in several ways [6]. Process mining strives to discover, monitor, and improve processes by extracting knowledge from event logs available in information systems [7]. The most commonly applied use case in process mining is discovering as-is process models that also serve as a starting point for more detailed analysis [8]. Based on the mined as-is-process, the use case of conformance checking helps to point out deviations from normative, predefined process models and actual process enactments (e.g., unintended handover of tasks, skipped activities, missed performance goals). As process mining analyzes information on an event-level, it also helps evaluate the actual process performance (e.g., measuring cycle times, interruptions, exceptions). In sum, process mining can help ensure process hygiene, constituting a fundamental requirement to achieve operational excellence [8]. As process mining is one of the most active streams in BPM, numerous approaches have been proposed in the last decade, and various commercial vendors transferred these methods into practice, substantially facilitating event data analysis [9]. At the tip of the iceberg, Celonis expanded in only seven years from start-up to a unicorn, indicating the enormous cross-industry business potential of process mining [10]. By 2023, Markets and Markets predicts a market potential of 1.42 billion US$ for process mining technologies [11]. However, there are still numerous unsolved challenges that hinder the further adoption and usage of process mining at the enterprise level [12]. First, finding, extracting, and preprocessing relevant event data is still challenging and requires a significant amount of time in a process mining project and, thus, remains a bottleneck without providing appropriate support [13]. Second, most process mining approaches operate on a single-process level, but organizations are confronted with a process network covering hundreds of interdependent processes [12]. Third, process managers strongly require forward-directed operational support, but most process mining approaches provide only descriptive ex-post insights, e.g., d
{"title":"Data-driven Management of Interconnected Business Processes - Contributions to Predictive and Prescriptive Process Mining","authors":"Wolfgang Kratsch","doi":"10.15495/EPUB_UBT_00005329","DOIUrl":"https://doi.org/10.15495/EPUB_UBT_00005329","url":null,"abstract":"Business process management (BPM) is an accepted paradigm of organizational design and a source of corporate performance [1]. Due to substantial progress in process identification, analysis, implementation, and improvement [2, 3], BPM receives constant attention from industry [4]. In times of market consolidation and increasing competition, operational excellence (i.e., continuously optimizing an organization’s processes in terms of effectiveness and efficiency) is key to staying competitive. While traditional research in BPM focused on process models and model-based information systems (e.g., workflow management systems), recently, the focus has shifted to datadriven methods such as process mining [5]. In contrast to model-driven BPM, process mining uses execution data in the form of events arising during process enactment, which may be exploited in several ways [6]. Process mining strives to discover, monitor, and improve processes by extracting knowledge from event logs available in information systems [7]. The most commonly applied use case in process mining is discovering as-is process models that also serve as a starting point for more detailed analysis [8]. Based on the mined as-is-process, the use case of conformance checking helps to point out deviations from normative, predefined process models and actual process enactments (e.g., unintended handover of tasks, skipped activities, missed performance goals). As process mining analyzes information on an event-level, it also helps evaluate the actual process performance (e.g., measuring cycle times, interruptions, exceptions). In sum, process mining can help ensure process hygiene, constituting a fundamental requirement to achieve operational excellence [8]. As process mining is one of the most active streams in BPM, numerous approaches have been proposed in the last decade, and various commercial vendors transferred these methods into practice, substantially facilitating event data analysis [9]. At the tip of the iceberg, Celonis expanded in only seven years from start-up to a unicorn, indicating the enormous cross-industry business potential of process mining [10]. By 2023, Markets and Markets predicts a market potential of 1.42 billion US$ for process mining technologies [11]. However, there are still numerous unsolved challenges that hinder the further adoption and usage of process mining at the enterprise level [12]. First, finding, extracting, and preprocessing relevant event data is still challenging and requires a significant amount of time in a process mining project and, thus, remains a bottleneck without providing appropriate support [13]. Second, most process mining approaches operate on a single-process level, but organizations are confronted with a process network covering hundreds of interdependent processes [12]. Third, process managers strongly require forward-directed operational support, but most process mining approaches provide only descriptive ex-post insights, e.g., d","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124277257","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 : 2020-09-14DOI: 10.1007/978-3-030-58638-6_14
R. Syed, S. Leemans, R. Eden, J. Buijs
{"title":"Process Mining Adoption - A Technology Continuity Versus Discontinuity Perspective","authors":"R. Syed, S. Leemans, R. Eden, J. Buijs","doi":"10.1007/978-3-030-58638-6_14","DOIUrl":"https://doi.org/10.1007/978-3-030-58638-6_14","url":null,"abstract":"","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124690401","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 : 2020-09-13DOI: 10.1007/978-3-030-58779-6_13
Rafael Cabello, María José Escalona Cuaresma, J. G. Enríquez
{"title":"Beyond the Hype: RPA Horizon for Robot-Human Interaction","authors":"Rafael Cabello, María José Escalona Cuaresma, J. G. Enríquez","doi":"10.1007/978-3-030-58779-6_13","DOIUrl":"https://doi.org/10.1007/978-3-030-58779-6_13","url":null,"abstract":"","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115377290","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 : 2020-09-13DOI: 10.1007/978-3-030-58666-9_3
F. Maggi, M. Montali, R. Peñaloza, Anti Alman
{"title":"Extending Temporal Business Constraints with Uncertainty","authors":"F. Maggi, M. Montali, R. Peñaloza, Anti Alman","doi":"10.1007/978-3-030-58666-9_3","DOIUrl":"https://doi.org/10.1007/978-3-030-58666-9_3","url":null,"abstract":"","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115679454","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 : 2020-09-13DOI: 10.1007/978-3-030-58638-6_7
Jari Peeperkorn, S. V. Broucke, Jochen De Weerdt
{"title":"Conformance Checking Using Activity and Trace Embeddings","authors":"Jari Peeperkorn, S. V. Broucke, Jochen De Weerdt","doi":"10.1007/978-3-030-58638-6_7","DOIUrl":"https://doi.org/10.1007/978-3-030-58638-6_7","url":null,"abstract":"","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116662403","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 : 2020-09-13DOI: 10.1007/978-3-030-58666-9_18
D. Fischer, Kanika Goel, R. Andrews, Christopher G. J. van Dun, M. Wynn, Maximilian Röglinger
{"title":"Enhancing Event Log Quality: Detecting and Quantifying Timestamp Imperfections","authors":"D. Fischer, Kanika Goel, R. Andrews, Christopher G. J. van Dun, M. Wynn, Maximilian Röglinger","doi":"10.1007/978-3-030-58666-9_18","DOIUrl":"https://doi.org/10.1007/978-3-030-58666-9_18","url":null,"abstract":"","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124005820","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 : 2020-09-13DOI: 10.1007/978-3-030-58666-9_5
Arik Senderovich, J. Schippers, H. Reijers
{"title":"Socially-Aware Business Process Redesign","authors":"Arik Senderovich, J. Schippers, H. Reijers","doi":"10.1007/978-3-030-58666-9_5","DOIUrl":"https://doi.org/10.1007/978-3-030-58666-9_5","url":null,"abstract":"","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116437254","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 : 2020-09-13DOI: 10.1007/978-3-030-58666-9_28
Owen Keates, M. Wynn, W. Bandara
{"title":"A Multi Perspective Framework for Enhanced Supply Chain Analytics","authors":"Owen Keates, M. Wynn, W. Bandara","doi":"10.1007/978-3-030-58666-9_28","DOIUrl":"https://doi.org/10.1007/978-3-030-58666-9_28","url":null,"abstract":"","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116446407","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 : 2020-09-13DOI: 10.1007/978-3-030-58779-6_12
J. M. López-Carnicer, C. D. Valle, J. G. Enríquez
{"title":"Towards an OpenSource Logger for the Analysis of RPA Projects","authors":"J. M. López-Carnicer, C. D. Valle, J. G. Enríquez","doi":"10.1007/978-3-030-58779-6_12","DOIUrl":"https://doi.org/10.1007/978-3-030-58779-6_12","url":null,"abstract":"","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122574032","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 : 2020-09-13DOI: 10.1007/978-3-030-58666-9_11
Luis Quishpi, J. Carmona, Lluís Padró
{"title":"Extracting Annotations from Textual Descriptions of Processes","authors":"Luis Quishpi, J. Carmona, Lluís Padró","doi":"10.1007/978-3-030-58666-9_11","DOIUrl":"https://doi.org/10.1007/978-3-030-58666-9_11","url":null,"abstract":"","PeriodicalId":143924,"journal":{"name":"International Conference on Business Process Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132847456","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：