Business Monitoring Framework for Process Discovery with Real-Life Logs

Abe, Mari; Kudo, Mineichi

doi:10.1007/978-3-319-10172-9_30

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Further approaches. In [47], [48], the authors introduce a monitoring framework for automated process discovery. A monitoring context is used to extract traces from relational event data and attach different types of metrics to them.…”

Section: State Machinesmentioning

confidence: 99%

“…The remaining 22 methods can discover constructs for parallelism, exclusive choice and loops, with the exception of [47], which can detect exclusive choice and loops but not parallelism, [84], which can detect parallelism and exclusive choice but not loops, and [67], which can discover exclusive choices only.…”

Section: Procedural Language Constructs (Rq3)mentioning

confidence: 99%

“…[56], [81]), automotive [12], healthcare [19], [72], [92], and project management and insurance (cf. [47], [79]).…”

Section: Evaluation Data and Domains (Rq5)mentioning

confidence: 99%

See 2 more Smart Citations

Automated Discovery of Process Models from Event Logs: Review and Benchmark

Augusto

Conforti

Dumas

et al. 2019

IEEE Trans. Knowl. Data Eng.

325

266

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Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering twelve publicly-available real-life event logs, twelve proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.

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Section: State Machinesmentioning

confidence: 99%

Section: Procedural Language Constructs (Rq3)mentioning

confidence: 99%

See 1 more Smart Citation

Automated Discovery of Process Models from Event Logs: Review and Benchmark

Augusto

Conforti

Dumas

et al. 2019

IEEE Trans. Knowl. Data Eng.

325

266

View full text Add to dashboard Cite

show abstract

Context-Aware Business Process Management

et al. 2021

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Context awareness is essential for successful business process management (BPM). So far, research has covered relevant BPM context factors and context-aware process design, but little is known about how to assess and select BPM methods in a context-aware manner. As BPM methods are involved in all stages of the BPM lifecycle, it is key to apply appropriate methods to efficiently use organizational resources. Following the design science paradigm, the study at hand addresses this gap by developing and evaluating the Context-Aware BPM Method Assessment and Selection (CAMAS) Method. This method assists method engineers in assessing in which contexts their BPM methods can be applied and method users in selecting appropriate BPM methods for given contexts. The findings of this study call for more context awareness in BPM method design and for a stronger focus on explorative BPM. They also provide insights into the status quo of existing BPM methods.

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Analyzing Business Processes by Automatically Detecting KPI Thresholds

Abe¹,

Kudo

2016

2016 IEEE International Conference on Services Computing (SCC)

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Business Monitoring Framework for Process Discovery with Real-Life Logs

Cited by 5 publications

References 11 publications

Automated Discovery of Process Models from Event Logs: Review and Benchmark

Automated Discovery of Process Models from Event Logs: Review and Benchmark

Context-Aware Business Process Management

Analyzing Business Processes by Automatically Detecting KPI Thresholds

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