Rediscovering workflow models from event-based data using little thumb

Weijters, A. J. M. M.; Aalst, Wil M. P. van der

doi:10.3233/ica-2003-10205

Cited by 400 publications

(347 citation statements)

References 13 publications

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“…A variety of techniques for process mining has been suggested in literature [10,11,8]. As illustrated in this paper, traditional process mining is different from process variant mining due to its different goal and inputs.…”

Section: Related Workmentioning

confidence: 99%

“…4 (see Fig. 5), and then used them as input for different process mining techniques: Alpha algorithm [8], Alpha++ algorithm [9], Heuristics mining [10], and Genetic mining [11]. (These are some of the most well-known algorithms for discovering process models from execution logs).…”

Section: Fig 4 One Examplementioning

confidence: 99%

“…Its key idea is to discover a process model by analyzing the execution behavior of (completed) process instances as captured in execution logs [8]. Different mining techniques like alpha algorithm [8], heuristics mining [10], and genetic mining [11] have been proposed in this context. When considering the extensive research on process mining, only little work has dealt with process variant mining so far.…”

Section: Introductionmentioning

confidence: 99%

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Mining Based on Learning from Process Change Logs

Reichert

Wombacher

2009

Business Process Management Workshops

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Abstract. In today's dynamic business world economic success of an enterprise increasingly depends on its ability to react to internal and external changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process changes. This, in turn, leads to a large number of process variants, which are created from the same original model, but might slightly differ from each other. This paper deals with issues related to the mining of such process variant collections. Our overall goal is to learn from process changes and to merge the resulting model variants into a generic process model in the best possible way. By adopting this generic process model in the PAIS, future costs of process change and need for process adaptations will decrease. We compare process variant mining with conventional process mining techniques, and show that it is additionally needed to learn from process changes.

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Section: Related Workmentioning

confidence: 99%

Section: Fig 4 One Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mining Based on Learning from Process Change Logs

Reichert

Wombacher

2009

Business Process Management Workshops

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“…According to the EDIFACT standards a Delivery just in time message (DELJIT) is intended to provide "firm deliver instructions" with fine-grained time granularity (i.e., days, hours) whereas Delivery schedule messages (DELFOR) are The values next to the activity labels indicate the number of events in the event log (i.e., the number of messages) that led to the recognition of the corresponding activity (see [25] for a detailed description of the elements in the graph).…”

Section: Conclusion and Reflectionmentioning

confidence: 99%

“…This assumed incompleteness of the data set suggests that a relatively robust mining algorithm should be used. Hence, we choose the Heuristics Miner plug-in of ProM 6 [25] for mining a business process model from the correlated EDI messages.…”

Section: Mining the Inter-organizational Business Process Modelmentioning

confidence: 99%

Mining Inter-organizational Business Process Models from EDI Messages: A Case Study from the Automotive Sector

Engel

Aalst

Zapletal

et al. 2012

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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Abstract. Traditional standards for Electronic Data Interchange (EDI), such as EDIFACT and ANSI X12, have been employed in Business-toBusiness (B2B) e-commerce for decades. Due to their wide industry coverage and long-standing establishment, they will presumably continue to play an important role for some time. EDI systems are typically not "process-aware", i.e., messages are standardized but processes simply "emerge". However, to improve performance and to enhance the control, it is important to understand and analyze the "real" processes supported by these systems. In the case study presented in this paper we uncover the inter-organizational business processes of an automotive supplier company by analyzing the EDIFACT messages that it receives from its business partners. We start by transforming a set of observed messages to an event log, which requires that the individual messages are correlated to process instances. Thereby, we make use of the specific structure of EDIFACT messages. Then we apply process mining techniques to uncover the inter-organizational business processes. Our results show that inter-organizational business process models can be derived by analyzing EDI messages that are exchanged in a network of organizations.

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Process Mining and Description

Sheu

Ramamoorthy³

et al. 2010

Semantic Computing

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Rediscovering workflow models from event-based data using little thumb

Cited by 400 publications

References 13 publications

Mining Based on Learning from Process Change Logs

Mining Based on Learning from Process Change Logs

Mining Inter-organizational Business Process Models from EDI Messages: A Case Study from the Automotive Sector

Process Mining and Description

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