A GraphML-based Visualization Framework for Workflow-Performers’ Closeness Centrality Measurements

A workflow model specifies execution sequences of the associated activities and their affiliated relationships with roles, performers, invoked-applications, and relevant data. These affiliated relationships exhibit a series of valuable human-centered organizational knowledge and are utilized for exploring human resource's work patterns. This paper focuses not only on a specific type of affiliated relationships between performers and activities, in particular, which forms a performer-to-activity affiliation network, but also on a specific type of analysis techniques, which builds a closeness centrality measurement approach for quantifying the degrees of farnesses between performers as well as between activities. In other words, this paper investigates a series of formal approaches for building organizational closeness centrality measurement techniques on the specific type of affiliation networks. The investigation mainly deploys two types of algorithmic formalisms along with an operational example, which are measuring performercentered organizational closeness centralities and activity-centered organizational closeness centralities, respectively. In order to validate the deployed algorithmic equations, the paper carries out a couple of operational experiments; One is on an ICN-based workflow package and the other is on a discovered workflow model mined from a dataset of workflow event logs. Summarily, this paper devises a series of algorithms and equations for measuring closeness centralities of activities, verify the devised algorithms and their related equations along with operational examples, and discuss the ultimate implications of these analysis techniques of organizational closeness centrality measurements as the performer-to-activity affiliation networking knowledge in workflow-supported organizations.

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ρ-Algorithm: A SICN-Oriented Process Mining Framework

Pham

Kim

2021

IEEE Access

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This paper devises an algorithmic process mining framework characterized by the mathematical process model of structured information control nets (SICN) and the concept of mass-driven ρ-function as a decision-making criterion of structural process patterns. In order to prove the functional correctness of the proposed algorithmic framework, this paper also implements all the related algorithms as a process mining system and carries out an operational experiment on a typical synthetic dataset of process enactment event logs prepared and released in the 4TU Centre for Research Data. The core outcome of the algorithmic framework development is the ρ-Algorithm that ought to be a novel approach not only for mining all the primitive process patterns, such as linear (sequential), disjunctive (selective-OR), conjunctive (parallel-AND), and repetitive (iterative-LOOP) process patterns, with perfectly keeping the structural properties of matched pairing and proper nesting, but also for reasonably discovering structured (even unstructured) information control nets from such IEEE XES-formatted datasets of process enactment event logs. The discovery functionality of the ρ-Algorithm is made up of three stepwise algorithms: STEP-1, STEP-2 and STEP-3 algorithms, and these algorithms are formally described as an algorithmic framework supported by the conceptual process mining archiecture with a series of theoretical concepts with the temporal workcase model and the temporal loop-case model. Finally, we validate the functional correctness as well as the discovery perfectness of the proposed algorithmic framework named as ρ-Algorithm by deploying the implemented ρ-Algorithm on a synthetic, non-noise and IEEE XES-formatted dataset of process enactment event logs recorded from the 10,000 work-cases with 113 activities of the Petrinet-oriented process model named as the Large Bank Transaction Process Model.

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A GraphML-based Visualization Framework for Workflow-Performers’ Closeness Centrality Measurements

Cited by 5 publications

References 15 publications

Formal approach for discovering work transference networks from workflow logs

Formal approach for discovering work transference networks from workflow logs

Organizational Closeness Centralities of Workflow-Supported Performer-to-Activity Affiliation Networks

ρ-Algorithm: A SICN-Oriented Process Mining Framework

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