DTMiner: A Tool for Decision Making Based on Historical Process Data

Obregon, Josue; Kim, Ae-Kyung; Jung, Jae‐Yoon

doi:10.1007/978-3-319-02922-1_6

Cited by 5 publications

(12 citation statements)

References 7 publications

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“…Most papers consider optimizing temporal perspectives (15 out of 21), such as cycle or processing time. More specifically, in Wibisono et al (2015) ; Kim, Obregon & Jung (2013) ; Obregon, Kim & Jung (2013) ; Thomas, Kumar & Annappa, (2017) , reducing cycle time is defined as the main objective. In this context, reducing cycle time means that the cycle time should be gradually improved by ensuring that each coming case takes less time than the previous average.…”

Section: Resultsmentioning

confidence: 99%

“…Several optimizing methods make use of techniques such as decision trees ( Sindhgatta, Ghose & Dam, 2016 ; Kim, Obregon & Jung, 2013 ; Obregon, Kim & Jung, 2013 ; Conforti et al, 2015 ; Ghattas, Soffer & Peleg, 2014 ). For example, Sindhgatta, Ghose & Dam (2016) use decision tree learning to predict the performance of an ongoing case considering process context and historical trace.…”

Section: Resultsmentioning

confidence: 99%

“…There are, however, papers that explain the approach in abstract terms and give references to techniques they use but do not describe them in detail (24% papers) ( e.g. , Barba, Weber & Valle, 2011 ; Obregon, Kim & Jung, 2013 ).…”

Section: Resultsmentioning

confidence: 99%

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Prescriptive process monitoring: Quo vadis?

Kubrak

Milani

Nolte

et al. 2022

PeerJ Computer Science

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Prescriptive process monitoring methods seek to optimize a business process by recommending interventions at runtime to prevent negative outcomes or address poorly performing cases. In recent years, various prescriptive process monitoring methods have been proposed. This article studies existing methods in this field via a systematic literature review (SLR). In order to structure the field, this article proposes a framework for characterizing prescriptive process monitoring methods according to their performance objective, performance metrics, intervention types, modeling techniques, data inputs, and intervention policies. The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods. This article highlights the need to validate existing and new methods in real-world settings, extend the types of interventions beyond those related to the temporal and cost perspectives, and design policies that take into account causality and second-order effects.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Prescriptive process monitoring: Quo vadis?

Kubrak

Milani

Nolte

et al. 2022

PeerJ Computer Science

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show abstract

“…Most papers consider optimizing the temporal perspective (15 out of 20), such as cycle or processing time. More specifically, in [45,21,31,40], reducing cycle time is defined as the main objective. For instance, Thomas et al [40] describe a method to minimize the cycle time of an environmental permit application process.…”

Section: Prescriptive Process Monitoring Objectivesmentioning

confidence: 99%

Prescriptive Process Monitoring: Quo Vadis?

Kubrak¹,

Milani²,

Nolte³

et al. 2021

Preprint

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Prescriptive process monitoring methods seek to optimize a business process by recommending interventions at runtime to prevent negative outcomes or poorly performing cases. In recent years, various prescriptive process monitoring methods have been proposed. This paper studies existing methods in this field via a Systematic Literature Review (SLR). In order to structure the field, the paper proposes a framework for characterizing prescriptive process monitoring methods according to their performance objective, performance metrics, intervention types, modeling techniques, data inputs, and intervention policies. The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods. The paper highlights the need to validate existing and new methods in real-world settings, to extend the types of interventions beyond those related to the temporal and cost perspectives, and to design policies that take into account causality and second-order effects.

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“…In particular, human resource allocation has been considered as a significant problem within the context of BPM (Huang et al , 2012b; Wibisono et al , 2015; Xu et al , 2008; Zhao and Zhao, 2014), due to the influence that the correct allocation may have on the performance of the process (Liu et al , 2014; Zhao and Zhao, 2014), on costs (Huang et al , 2011; Obregon et al , 2013), and on the efficient use of resources during the process execution (Fadol et al , 2015; Kumar et al , 2002; Xu et al , 2008). As such, different approaches have been proposed in the literature with the aim of providing improved support to make the task of resource allocation more efficient, both to support the decision making of the individual in charge of the relevant process when selecting a candidate, as well as during the allocation of a resource for executing each process activity.…”

Section: Introductionmentioning

confidence: 99%

Human resource allocation in business process management and process mining

Arias

Saavedra

Marques

et al. 2018

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Purpose-Human resource allocation is considered a relevant problem in business process management (BPM). The successful allocation of available resources for the execution of process activities can impact on process performance, reduce costs and obtain a better productivity of the resources. In particular, process mining is an emerging discipline that allows improvement of the resource allocation based on the analysis of historical data. The purpose of this paper is to provide a broad review of primary studies published in the research area of human resource allocation in BPM and process mining. Design/methodology/approach-A systematic mapping study (SMS) was conducted in order to classify the proposed approaches to allocate human resources. A total of 2,370 studies published between January 2005 and July 2016 were identified. Through a selection protocol, a group of 95 studies were selected. Findings-Human resource allocation is an emerging research area that has been evolving over time, generating new proposals that are increasingly applied to real case studies. The majority of proposed approaches relate to the period 2011-2016. Journals and conference proceedings are the most common venues. Validation research and evaluation research are the most common research types. There are two main evaluation methods: simulation and case studies. Originality/value-This study aims to provide an initial assessment of the state of the art in the research area of human resource allocation in BPM and process mining. To the best of the authors' knowledge, this is the first research that has been conducted to date that generates a SMS in this research area.

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DTMiner: A Tool for Decision Making Based on Historical Process Data

Cited by 5 publications

References 7 publications

Prescriptive process monitoring: Quo vadis?

Prescriptive process monitoring: Quo vadis?

Prescriptive Process Monitoring: Quo Vadis?

Human resource allocation in business process management and process mining

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