Strategies to Automatically Derive A Process Model From A Configurable Process Model Based on Event Data

Arriagada-Benítez, Mauricio; Sepúlveda, Marcos; Muñoz-Gama, Jorge; Buijs, Jorn op den

doi:10.20944/preprints201709.0003.v1

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…This study shows that almost all selected papers in this comparative study had dealt with changes in a single business process [4][5] [6]. And only a few papers have proposed approaches to deal with changes in a collection of business processes [24] [25]. Those approaches are limited to propose some rules [24] related specially to the configuration and how process variants could be created from the configurable process model based on the observed behaviors [25].…”

Section: Related Workmentioning

confidence: 99%

“…And only a few papers have proposed approaches to deal with changes in a collection of business processes [24] [25]. Those approaches are limited to propose some rules [24] related specially to the configuration and how process variants could be created from the configurable process model based on the observed behaviors [25]. However, they do not detect changes over time which are known as concept drift.…”

Section: Related Workmentioning

confidence: 99%

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Handling Sudden and Recurrent Changes in Business Process Variability: Change Mining based Approach

Hmami¹,

Sbai²,

Fredj³

2021

IJACSA

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Changes are random and unavoidable actions in business processes, and they are frequently overlooked by managers, especially when managers need to deal with a collection of process variants. Because they must manage every single business process variant separately which is a timeconsuming task. They exist many approaches to manage a collection of business process and deal with variability. Such as process mining approaches, that can discover configurable business process models, enhancing them and verify conformity automatically. However, those approaches do not cover changes and concept drift that occur over time. This paper presents a novel change mining approach that discovers changes in a collection of event logs and reports them on a change log. This change log can be analyzed to determine whether the changes are sudden or recurrent and recommend afterward some improvement to the configurable process model.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Handling Sudden and Recurrent Changes in Business Process Variability: Change Mining based Approach

Hmami¹,

Sbai²,

Fredj³

2021

IJACSA

View full text Add to dashboard Cite

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Functional Integration with Process Mining and Process Analyzing for Structural and Behavioral Properness Validation of Processes Discovered from Event Log Datasets

Kim

2020

Applied Sciences

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In this paper, we propose an integrated approach for seamlessly and effectively providing the mining and the analyzing functionalities to redesigning work for very large-scale and massively parallel process models that are discovered from their enactment event logs. The integrated approach especially aims at analyzing not only their structural complexity and correctness but also their animation-based behavioral properness, and becomes concretized to a sophisticated analyzer. The core function of the analyzer is to discover a very large-scale and massively parallel process model from a process log dataset and to validate the structural complexity and the syntactical and behavioral properness of the discovered process model. Finally, this paper writes up the detailed description of the system architecture with its functional integration of process mining and process analyzing. More precisely, we excogitate a series of functional algorithms for extracting the structural constructs and for visualizing the behavioral properness of those discovered very large-scale and massively parallel process models. As experimental validation, we apply the proposed approach and analyzer to a couple of process enactment event log datasets available on the website of the 4TU.Centre for Research Data.

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Special Issue on Modeling, Simulation, Operation and Control of Discrete Event Systems

Zhou

et al. 2018

Applied Sciences

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Information and computer technologies provide the spur to burgeoning man-made, highly automated systems. Nowadays, discrete event systems (DES), as a natural abstraction of various contemporary technological applications, include intelligent urban traffic systems, automated flexible manufacturing systems, computer networks, communication protocols, logistic systems, monitoring and control of large buildings, scientific and business workflows, distributed databases, and concurrent software systems. Their modeling, simulation, operation, scheduling and control are the primary issues to be investigated. It is of paramount significance to develop novel formal frameworks, analysis techniques, design tools, testing methods, and systematic control and optimization procedures for these kinds of man-made, highly complex systems. This is critical for their development and survivability [1][2][3][4][5][6].Focusing on supervisory control, deadlock analysis, scheduling, resource management, performance evaluation, system identification, fault diagnosis, and performance evaluation, as well as pertinent problems of DES, this special issue aims to make a timely collection of recent advances in the area. There were 77 manuscripts submitted to this special issue, and 19 papers were finally accepted for publication. The acceptance rate was 24.6%.The first paper [7], authored by Yao and Li, investigates the input-output finite time stabilization of time-varying impulsive positive hybrid systems based on finite state machines through the mode-dependent average dwell time technique. An output feedback controller is proposed that can stabilize non-autonomous hybrid systems by formulating the considered problem as a linear program. The second paper [8], authored by Yang, Peng, Ju, Xu, Yin, and Huang, is concerned with the efficiency improvement of multi-agent simulation. To address the problem of resynchronization interval selection and cyclic dependency, this paper presents a lookahead behavior model where a minimal safe time window is used to predict the interactions among implicit models. Consequently, the resynchronization interval can be efficiently determined.The third paper [9], authored by Kucharska, proposes a heuristic decision-making approach for an NP-hard scheduling problem based on an algebraic-logical meta-model. By constructing an acceptable solution only, it significantly reduces the calculation cost. A general algorithm is developed and applied to the scheduling problems of unrelated parallel machines with a deadline and machine setup time dependent on the process state. The fourth paper [10], authored by Choi, Seo, and Kim, deals with discrete event system simulation, aiming to improve simulation efficiency by using a multi-fidelity modeling framework, while keeping simulation accuracy loss minimized. A number of novel concepts, such as an interest region, a fidelity change condition, and a selection model, are defined and integrated into the framework.

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Strategies to Automatically Derive A Process Model From A Configurable Process Model Based on Event Data

Cited by 4 publications

References 22 publications

Handling Sudden and Recurrent Changes in Business Process Variability: Change Mining based Approach

Handling Sudden and Recurrent Changes in Business Process Variability: Change Mining based Approach

Functional Integration with Process Mining and Process Analyzing for Structural and Behavioral Properness Validation of Processes Discovered from Event Log Datasets

Special Issue on Modeling, Simulation, Operation and Control of Discrete Event Systems

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