Discovering Stochastic Petri Nets with Arbitrary Delay Distributions from Event Logs

Solti, Andreas; Aalst, Wil M. P. van der; Weske, Mathias

doi:10.1007/978-3-319-06257-0_2

Cited by 57 publications

(61 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Models are discovered from and validated against event data from recorded process executions, see [43]. Mined models are used as the basis for prediction [44,45], simulation [46], and resource-behavior analysis [47,48].…”

Section: Related Workmentioning

confidence: 99%

Conformance checking and performance improvement in scheduled processes: A queueing-network perspective

Senderovich

Weidlich

Yedidsion

et al. 2016

Information Systems

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Conformance checking and performance improvement in scheduled processes: A queueing-network perspective

Senderovich

Weidlich

Yedidsion

et al. 2016

Information Systems

View full text Add to dashboard Cite

“…For our purposes, we reuse the existing work in the ProM framework that extracts performance information of activities from an event log and enriches plain Petri nets to GDT_SPN models [3]. In [3], we discuss the challenges for discovering GDT_SPN models with respect to selected execution semantics of the model.…”

Section: Definition 2 (Petri Net)mentioning

confidence: 99%

“…In [3], we discuss the challenges for discovering GDT_SPN models with respect to selected execution semantics of the model. The discovery algorithm uses replaying techniques, cf.…”

Section: Definition 2 (Petri Net)mentioning

confidence: 99%

See 1 more Smart Citation

Improving Documentation by Repairing Event Logs

Solti

Mans

Aalst

et al. 2013

Lecture Notes in Business Information Processing

Self Cite

View full text Add to dashboard Cite

Summary. In enterprises, business process models are used for capturing as-is business processes. During process enactment correct documentation is important to ensure quality, to support compliance analysis, and to allow for correct accounting. Missing documentation of performed activities can be directly translated into lost income, if accounting is based on documentation. Still, many processes are manually documented in enterprises. As a result, activities might be missing from the documentation, even though they were performed. In this paper, we make use of process knowledge captured in process models, and provide a method to repair missing entries in the logs. The repaired logs can be used for direct feedback to ensure correct documentation, i.e., participants can be asked to check, whether they forgot to document activities that should have happened according to the process models. We realize the repair by combining stochastic Petri nets, alignments, and Bayesian networks. We evaluate the results using both synthetic data and real event data from a Dutch hospital.

show abstract

“…However, if steps are recorded manually this may lead to misleading results as little weight is given to a priori domain knowledge. Therefore, we adopt a stochastic approach to modeling process behavior and introduce a novel approach to repair event logs according to a given stochastically enriched process model [2]. To model the as-is process we use Petri nets enhanced with stochastic timing information and path probabilities.…”

Section: Introductionmentioning

confidence: 99%

Repairing Event Logs Using Timed Process Models

Solti

Mans

Aalst

et al. 2013

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. Process mining aims to infer meaningful insights from process-related data and attracted the attention of practitioners, tool-vendors, and researchers in recent years. Traditionally, event logs are assumed to describe the as-is situation. But this is not necessarily the case in environments where logging may be compromised due to manual logging. For example, hospital staff may need to manually enter information regarding the patient's treatment. As a result, events or timestamps may be missing or incorrect.In this work, we make use of process knowledge captured in process models, and provide a method to repair missing events in the logs. This way, we facilitate analysis of incomplete logs. We realize the repair by combining stochastic Petri nets, alignments, and Bayesian networks.

show abstract

Discovering Stochastic Petri Nets with Arbitrary Delay Distributions from Event Logs

Cited by 57 publications

References 13 publications

Conformance checking and performance improvement in scheduled processes: A queueing-network perspective

Conformance checking and performance improvement in scheduled processes: A queueing-network perspective

Improving Documentation by Repairing Event Logs

Repairing Event Logs Using Timed Process Models

Contact Info

Product

Resources

About