Generating a Process Model from a Process Audit Log

Golani, Mati; Pinter, Shlomit S.

doi:10.1007/3-540-44895-0_10

Cited by 21 publications

(6 citation statements)

References 19 publications

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“…In [47], the authors do not use partial orders as an intermediary object. However, they do account for multiple activity stages, e.g., ready, started, etc.…”

Section: Certain Semanticsmentioning

confidence: 99%

Partial-order-based process mining: a survey and outlook

Leemans

Zelst

2022

Knowl Inf Syst

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The field of process mining focuses on distilling knowledge of the (historical) execution of a process based on the operational event data generated and stored during its execution. Most existing process mining techniques assume that the event data describe activity executions as degenerate time intervals, i.e., intervals of the form [t, t], yielding a strict total order on the observed activity instances. However, for various practical use cases, e.g., the logging of activity executions with a nonzero duration and uncertainty on the correctness of the recorded timestamps of the activity executions, assuming a partial order on the observed activity instances is more appropriate. Using partial orders to represent process executions, i.e., based on recorded event data, allows for new classes of process mining algorithms, i.e., aware of parallelism and robust to uncertainty. Yet, interestingly, only a limited number of studies consider using intermediate data abstractions that explicitly assume a partial order over a collection of observed activity instances. Considering recent developments in process mining, e.g., the prevalence of high-quality event data and techniques for event data abstraction, the need for algorithms designed to handle partially ordered event data is expected to grow in the upcoming years. Therefore, this paper presents a survey of process mining techniques that explicitly use partial orders to represent recorded process behavior. We performed a keyword search, followed by a snowball sampling strategy, yielding 68 relevant articles in the field. We observe a recent uptake in works covering partial-order-based process mining, e.g., due to the current trend of process mining based on uncertain event data. Furthermore, we outline promising novel research directions for the use of partial orders in the context of process mining algorithms.

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“…In [47], the authors do not use partial orders as an intermediary object. However, they do account for multiple activity stages, e.g., ready, started, etc.…”

Section: Certain Semanticsmentioning

confidence: 99%

Partial-order-based process mining: a survey and outlook

Leemans

Zelst

2022

Knowl Inf Syst

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“…Many papers on process mining have been published recently (e.g., [6,7,10,12,14,20,11]). Most of them focus on mining the control flow (control flow mining).…”

Section: The Process Mining Contextmentioning

confidence: 99%

Mining Staff Assignment Rules from Event-Based Data

Rinderle

Dadam

et al. 2006

Business Process Management Workshops

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Process mining offers methods and techniques for capturing process behaviour from log data of past process executions. Although many promising approaches on mining the control flow have been published, no attempt has been made to mine the staff assignment situation of business processes. In this paper, we introduce the problem of mining staff assignment rules using history data and organisational information (e.g., an organisational model) as input. We show that this task can be considered an inductive learning problem and adapt a decision tree learning approach to derive staff assignment rules. In contrast to rules acquired by traditional techniques (e.g., questionnaires) the thus derived rules are objective and show the staff assignment situation at hand. Therefore, they can help to better understand the process. Moreover, the rules can be used as input for further analysis, e.g., workload balance analysis or delta analysis. This paper presents the current state of our work and points out some challenges for future research.

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“…The main objective of process mining is to effectively use automatically collected data in order to gain process knowledge from the logs. In particular, process mining extracts formal process models from execution logs [3,8,9,10,11]. So far, the focus has been put on issues related to control-flow mining.…”

Section: Process Miningmentioning

confidence: 99%

Towards a Framework for the Agile Mining of Business Processes

Weber

Reichert

Rinderle

et al. 2006

Business Process Management Workshops

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In order to support business processes effectively, their implementation by a process management systems (PMS) must be as close to the real world's processes as possible. Generally, it is not sufficient to analyze and model a business process only once, and then to handle respective business cases according to the defined model for a long period of time. Instead, process implementations must be quickly adaptable to changing needs. A PMS should enable process instance changes and provide facilities for analyzing these instance-specific changes in order to derive optimized process models. In this paper we introduce a framework for the agile mining of business processes which supports the whole process life cycle in an integrated way. Our framework is based on process mining techniques, adaptive process management, and conversational case-based reasoning. On the one hand, it allows annotating execution and change logs with semantical information to gather information about the reasons for ad-hoc deviations, which can then be analyzed by the process engineer (with support from the PMS). On the other hand, it enables the process engineer to adapt process models based on the outcome of these analyses and to migrate related process instances to the new model.

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Generating a Process Model from a Process Audit Log

Cited by 21 publications

References 19 publications

Partial-order-based process mining: a survey and outlook

Partial-order-based process mining: a survey and outlook

Mining Staff Assignment Rules from Event-Based Data

Towards a Framework for the Agile Mining of Business Processes

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