Applying Process Mining to Support Management of Predictive Analytics/Data Mining Projects in a Decision Making Center

Sanchez-Escobar, Marlene Ofelia; Espinosa, Rafael Lozano; Espinosa, José Martín Molina; Noguez, Julieta; Vargas-Solar, Genoveva

doi:10.1109/icsai48974.2019.9010135

Cited by 3 publications

(8 citation statements)

References 14 publications

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“…The Process Mining (PM) technique is a reverse engineering approach where process models are generated using event logs [2]. In [3], the author classifies the following PM techniques that we use for our analysis: discover, conformance, and enhancement.…”

Section: Process Mining Techniques and Project Management Applicationsmentioning

confidence: 99%

“…We discussed in [2] the importance of integrating PA/DM processes with DMC processes, and we make an integration effort. However, PA/DM processes at the level of activity have not been studied separately.…”

Section: Pa/dm Processes With Crisp-dmmentioning

confidence: 99%

“…Finally, the authors developed a previous study to analyze PA/DM processes in a DMC [2]. Nevertheless, the paper is oriented to analyze project phases and not project activities.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Decision-Making Centers (DMCs) are immersive virtual environments used to understand complex problems, simplify decision-making, and visualize the results of predictive and scenario-based models [1]. These environments depend on the creation process of tools like: Predictive Analytics/Data Mining (PA/DM) models to operate [2]. Nevertheless, the authors have demonstrated in previous studies that DMC processes focus on high-level tasks and exclude detailed and standard PA/DM activities [2].…”

Section: Introductionmentioning

confidence: 99%

“…These environments depend on the creation process of tools like: Predictive Analytics/Data Mining (PA/DM) models to operate [2]. Nevertheless, the authors have demonstrated in previous studies that DMC processes focus on high-level tasks and exclude detailed and standard PA/DM activities [2]. The absence of commonality in PA/DM project execution, generates issues, since (1) models are built using empiric methodologies and (2) managers cannot follow up specific technical activities since they are different in every project.…”

Section: Introductionmentioning

confidence: 99%

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Supporting the Management of Predictive Analytics Projects in a Decision-Making Center using Process Mining

Sanchez-Escobar¹,

Noguez²,

Molina-Espinosa³

et al. 2021

Adv. sci. technol. eng. syst. j.

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Decision-Making Centers (DMCs) Environment facilitates stakeholders' decision-making processes using predictive models and diverse what-if scenarios. An essential element of this environment is the management of Decision Support Components (e.g., models or systems) that need to be created with mature methodologies and good delivery time. However, there has been a gap in the understanding of project management best practices in DMC environments and in the application of methodologies to ease project execution. In the following paper, we address that gap by analyzing six predictive analytics projects executed in a Mexican DMC using Process Mining techniques. We perform process discovery using a detailed activity event log, which has not been possible in previous studies. Additionally, we perform a compliance evaluation versus the de facto methodology to identify the current process alignment gaps, and finally, we analyze the social networks present in the process execution. The research reveals that (1) process mining models are helpful to address management issues of PA/DM projects (2) PA/DM projects require alignment to mature methodologies to improve process performance and avoid execution problems (3) PA/DM project execution should be revised at the activity level to identify issues and to propose specific strategies. This study's findings can help project managers to perform process analyses and to make informed decisions in PA/DM projects. The following paper is an extension of the article "Applying Process Mining to Support Management of Predictive Analytics/Data Mining Projects in a Decision-Making Center¨ presented in the 2019 International Conference on Systems and Informatics (ICSAI 2019).

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Section: Process Mining Techniques and Project Management Applicationsmentioning

confidence: 99%

Section: Pa/dm Processes With Crisp-dmmentioning

confidence: 99%

“…Finally, the authors developed a previous study to analyze PA/DM processes in a DMC [2]. Nevertheless, the paper is oriented to analyze project phases and not project activities.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Supporting the Management of Predictive Analytics Projects in a Decision-Making Center using Process Mining

Sanchez-Escobar¹,

Noguez²,

Molina-Espinosa³

et al. 2021

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

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Study of Learners Behaviour in virtual learning environment using Process Mining

Patil

2021

2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

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Policy Design for Electricity Efficiency: A Case Study of Bottom-Up Energy Modeling in the Residential Sector and Buildings

Sanchez-Escobar,

Noguez,

Molina-Espinosa

et al. 2023

Energies

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Energy models play a crucial role in the domain of energy policy by serving as essential instruments for decision-making. However, a significant limitation of numerous bottom-up energy models (BUEMs) is their empirical design, which hinders their ability to effectively inform policy design. This study presents a structured framework that can be used to improve the effectiveness of behavior, understanding, and engagement measures in the development of BUEMs for enhancing energy end-use efficiency. The model selected for this case study was provided by the Mexican Commission for the Efficient Use of Energy (CONUEE), and it examines the impact of regulatory instruments on the residential sector and residential buildings. The benefits of the proposed framework were successfully demonstrated through a quantitative comparison of real energy models, using and without using the said framework, revealing the advantages of its use. The framework significantly decreases the time required for model generation in various aspects by 59.43%. The obtained results highlight the effectiveness of the framework, and it could enhance the existing knowledge in the sector.

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Applying Process Mining to Support Management of Predictive Analytics/Data Mining Projects in a Decision Making Center

Cited by 3 publications

References 14 publications

Supporting the Management of Predictive Analytics Projects in a Decision-Making Center using Process Mining

Supporting the Management of Predictive Analytics Projects in a Decision-Making Center using Process Mining

Study of Learners Behaviour in virtual learning environment using Process Mining

Policy Design for Electricity Efficiency: A Case Study of Bottom-Up Energy Modeling in the Residential Sector and Buildings

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