Discovering Block-Structured Process Models from Event Logs - A Constructive Approach

Leemans, Sander J. J.; Fahland, Dirk; Aalst, Wil M. P. van der

doi:10.1007/978-3-642-38697-8_17

Cited by 408 publications

(360 citation statements)

References 27 publications

Supporting

Mentioning

354

Contrasting

Unclassified

Order By: Relevance

“…They provide a natural, structured, well-defined way of describing processes that are often easily translatable to Petri nets. The process tree formalisms used in [8,17,18] guarantee soundness as well. Process tree discovery techniques have also been proposed before.…”

Section: Related Workmentioning

confidence: 99%

“…A notion closely related to incompleteness is rediscoverability. If a process discovery technique has rediscoverability, it is able to discover models that have the same language as the real-life process by which a log was produced [3,5,17]. Figure 1 shows the context of process discovery, rediscoverability, and how discovered models can be evaluated.…”

Section: Introductionmentioning

confidence: 99%

“…Rediscoverability is usually proven using assumptions about both log and model [3,5,17]. A model must be from a certain class, and a log must contain sufficient information.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, the strongest completeness notion is language-completeness, i.e., each trace through the process must be present in the log. The weakest completeness notion is that each process step must occur at least once in the log: activity-completeness [17].…”

Section: Introductionmentioning

confidence: 99%

“…The Inductive Miner (IM) [17] is an example of an algorithm that discovers process trees and for which rediscoverability has been proven. IM applies a divide-and-conquer approach: it partitions the activities, selects the most important process construct, splits the log and recurses until a base case is encountered.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Discovering Block-Structured Process Models from Incomplete Event Logs

Leemans

Fahland

Aalst

2014

Application and Theory of Petri Nets and Concurrency

Self Cite

115

View full text Add to dashboard Cite

One of the main challenges in process mining is to discover a process model describing observed behaviour in the best possible manner. Since event logs only contain example behaviour and one cannot assume to have seen all possible process executions, process discovery techniques need to be able to handle incompleteness. In this paper, we study the effects of such incomplete logs on process discovery. We analyse the impact of incompleteness of logs on behavioural relations, which are an abstraction often used by process discovery techniques. We introduce probabilistic behavioural relations that are less sensitive to incompleteness, and exploit these relations to provide a more robust process discovery algorithm. We prove this algorithm to be able to rediscover a model of the original system. Furthermore, we show in experiments that our approach even rediscovers models from incomplete event logs that are much smaller than required by other process discovery algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Rediscoverability is usually proven using assumptions about both log and model [3,5,17]. A model must be from a certain class, and a log must contain sufficient information.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Discovering Block-Structured Process Models from Incomplete Event Logs

Leemans

Fahland

Aalst

2014

Application and Theory of Petri Nets and Concurrency

Self Cite

115

View full text Add to dashboard Cite

show abstract

Petri net‐based deviation detection between a process model with loop semantics and event logs

Wang

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

Summary Information systems have been widely used to realize business processes. The business processes can be described by formal or semi‐formal models. In practice, there are a number of realistic processes involving loop semantics. As an insufficient design or improvement of the information system, event logs generated from these systems may not be consistent with the existing models. By comparing an existing process model with the event logs, we can detect inconsistencies called deviations and verify and extend the business process model. In this paper, some abnormal activities in business processes are formally defined based on Petri nets. An efficient approach to detect deviations between the process model with loop structures and event logs is proposed. A clinical process in a healthcare information system is used as a case study to illustrate our work. Experimental results show the effectiveness and efficiency of the presented approach.

show abstract

Sampling business process event logs using graph‐based ranking model

Liu

Pei

Cheng

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

Summary Modern information systems are continuously collecting and storing large volumes of business process event logs. The analysis of event logs can provide valuable insights for business process re‐engineering and enhancement. Process discovery, as one of the most challenging event log analysis techniques, aims to discover a business process model from an event log. Many process discovery approaches have been proposed in the past two decades, however, most of them suffer from efficiency problem when dealing with large‐scale event logs. Motivated by PageRank, we propose LogRank, a graph‐based ranking model, for event log sampling in this paper. The LogRank is capable of sampling a large‐scale event log to a smaller size that can be efficiently handled by existing discovery approaches. To support real‐life applications, we instantiate the LogRank model for two typical types of event logs, that is, simple event logs and lifecycle event logs. To quantify the quality of a sample log with respect to the original one, we introduce a general evaluation framework that can be instantiated for different quality metrics. The proposed sampling approach has been implemented in the open‐source process mining toolkit ProM. By experiments with both synthetic and real‐life event logs, we demonstrate that the proposed LogRank‐based sampling approach provides an effective means to improve process discovery efficiency as well as guaranteeing high quality of discovered models.

show abstract

Discovering Block-Structured Process Models from Event Logs - A Constructive Approach

Cited by 408 publications

References 27 publications

Discovering Block-Structured Process Models from Incomplete Event Logs

Discovering Block-Structured Process Models from Incomplete Event Logs

Petri net‐based deviation detection between a process model with loop semantics and event logs

Sampling business process event logs using graph‐based ranking model

Contact Info

Product

Resources

About