Quality-Aware Resource Model Discovery

Context-aware process mining aims at extending a contemporary approach with process contexts for realistic process modeling. Regarding this discipline, there have been several attempts to combine process discovery and predictive process modeling and context information, e.g., time and cost. The focus of this paper is to develop a new method for deriving a quality-aware resource model. It first generates a resource-oriented transition system and identifies the quality-based superior and inferior cases. The quality-aware resource model is constructed by integrating these two results, and we also propose a model simplification method based on statistical analyses for better resource model visualization. This paper includes tooling support for our method, and one of the case studies on a semiconductor manufacturing process is presented to validate the usefulness of the proposed approach. We expect our work is practically applicable to a range of fields, including manufacturing and healthcare systems.

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Improving Healthcare Systems through Process Mining

2020 IEEE 23rd International Multitopic Conference (INMIC) ◽

10.1109/inmic50486.2020.9318159 ◽

2020 ◽

Author(s):

Ayesha Hakim

Keyword(s):

Process Mining ◽

Healthcare Systems

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Context-aware process mining framework for business process flexibility

Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services - iiWAS '10 ◽

10.1145/1967486.1967552 ◽

2010 ◽

Cited By ~ 3

Author(s):

Zerari Mounira ◽

Boufaida Mahmoud

Keyword(s):

Business Process ◽

Process Mining ◽

Context Aware ◽

Process Flexibility

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Context aware Hidden Markov Model for Intention process mining

10.1109/icds53782.2021.9626765 ◽

2021 ◽

Author(s):

Imane Choukri ◽

Hatim Guermah ◽

Abdelmajid Daosabah ◽

Mahmoud Nassar

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Process Mining ◽

Hidden Markov ◽

Context Aware

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Translating Workflow Nets to Process Trees: An Algorithmic Approach

Algorithms ◽

10.3390/a13110279 ◽

2020 ◽

Vol 13 (11) ◽

pp. 279

Author(s):

Sebastiaan J. van Zelst ◽

Sander J. J. Leemans

Keyword(s):

Process Modeling ◽

Time Complexity ◽

Explicit Knowledge ◽

Process Mining ◽

Behavioral Control ◽

Control Flow ◽

Algorithmic Approach ◽

Modeling Formalism ◽

Mining Algorithms ◽

Process Tree

Since their introduction, process trees have been frequently used as a process modeling formalism in many process mining algorithms. A process tree is a (mathematical) tree-based model of a process, in which internal vertices represent behavioral control-flow relations and leaves represent process activities. Translation of a process tree into a sound workflow net is trivial. However, the reverse is not the case. Simultaneously, an algorithm that translates a WF-net into a process tree is of great interest, e.g., the explicit knowledge of the control-flow hierarchy in a WF-net allows one to reason on its behavior more easily. Hence, in this paper, we present such an algorithm, i.e., it detects whether a WF-net corresponds to a process tree, and, if so, constructs it. We prove that, if the algorithm finds a process tree, the language of the process tree is equal to the language of the original WF-net. The experiments conducted show that the algorithm’s corresponding implementation has a quadratic time complexity in the size of the WF-net. Furthermore, the experiments show strong evidence of process tree rediscoverability.

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