An experimental mining and analytics for discovering proportional process patterns from workflow enactment event logs

Kim, Kyoung-Sook; Lee, Young-Koo; Ahn, Hyun; Kim, Kyoung-Sook

doi:10.1007/s11276-018-01899-z

Cited by 7 publications

(10 citation statements)

References 7 publications

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“…Consequently, in the next consecutive subsections we excogitate the details of the concrete goal and functional scope for developing the integrated architecture and system supporting the process mining functionality and the sophisticated process analyzing functionality. Figure 1 illustrates a situational snapshot of the process-aware enterprise and organization working with the Miningtime subsystem supporting the process discovery and rediscovery functions [26,27], and the Buildtime subsystem supporting the structural analysis functions and the visual-simulation functions. Particularly, we emphasize that the discovery is to explore business-activity processes from the event logs of the executions of the traditional information systems, while the rediscovery is to do mining of the enacted business-activity processes from the event logs and traces stored whenever the corresponding business-activity processes are enacted and executed by their process management system.…”

Section: An Integrated Functional Architecturementioning

confidence: 99%

“…The conceptual initiation of the process mining framework is introduced in this subsection. Especially, the core component of the framework is the ρ-Algorithm [26,29]. In the ρ-Algorithm, the symbol and name of rho (ρ) comes from the A programming language (APL) firstly released in 1960s.…”

Section: The Process Mining Approach: ρ-Algorithmmentioning

confidence: 99%

“…First, the process mining framework is able not only to rediscover the process patterns but also to discover the enactment proportions [27] of the process patterns from a dataset of the IEEE XES-formatted enactment event logs of a corresponding process model. − Second, the process mining framework is theoretically supported by the information control nets modeling methodology [16] of process models.…”

mentioning

confidence: 99%

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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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Section: An Integrated Functional Architecturementioning

confidence: 99%

Section: The Process Mining Approach: ρ-Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

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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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“…next consecutive subsections we excogitate the details of the concrete goal and functional scope for developing the integrated architecture and system supporting the process mining functionality and the sophisticated process analyzing functionality. Figure 1 illustrates a situational snapshot of the process-aware enterprise and organization working with the Miningtime subsystem supporting the process discovery and rediscovery functions [26] [27] and the Buildtime subsystem supporting the structural analysis functions as well as the visual-simulation functions. Particularly, we emphasize that the discovery is to explore business-activity processes from the event logs of the executions of the traditional information systems, while the rediscovery is to do mining the enacted business-activity processes from the event logs and traces stored whenever the corresponding business-activity processes are enacted and executed by their process management system.…”

Section: Related Workmentioning

confidence: 99%

“…− First, the process mining framework is able not only to rediscover the process patterns but also to discover the enactment proportions [27] and discover the enactment occurrences and proportions of each branch of the process patterns.…”

Section: Process Mining Functionalitymentioning

confidence: 99%

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

Kim¹

2020

Preprint

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Process (or business process) management systems fulfill defining, executing, monitoring and managing process models deployed on process-aware enterprises. Accordingly, the functional formation of the systems is made up of three subsystems such as modeling subsystem, enacting subsystem and mining subsystem. In recent times, the mining subsystem has been becoming an essential subsystem. Many enterprises have successfully completed the introduction and application of the process automation technology through the modeling subsystem and the enacting subsystem. According as the time has come to the phase of redesigning and reengineering the deployed process models, from now on it is important for the mining subsystem to cooperate with the analyzing subsystem; the essential cooperation capability is to provide seamless integrations between the designing works with the modeling subsystem and the redesigning work with the mining subsystem. In other words, we need to seamlessly integrate the discovery functionality of the mining subsystem and the analyzing functionality of the modeling subsystem. This integrated approach might be suitable very well when those deployed process models discovered by the mining subsystem are complex and very large-scaled, in particular. In this paper, we propose an integrated approach for seamlessly as well as effectively providing the mining and the analyzing functionalities to the redesigning work on 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. And more precisely, we excogitate a series of functional algorithms for extracting the structural constructs as well as for visualizing the behavioral properness on 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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Editorial: Big data technologies and applications

Pan

Barnaghi

et al. 2021

Wireless Netw

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An experimental mining and analytics for discovering proportional process patterns from workflow enactment event logs

Cited by 7 publications

References 7 publications

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

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

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

Editorial: Big data technologies and applications

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