ρ-Algorithm: A SICN-Oriented Process Mining Framework

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
$\rho $
-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 contribution of the paper is just the algorithmic framework development named as the
$\rho $
-Algorithm, which 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 mining functionality of the
$\rho $
-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 architecture with a series of theoretical concepts with the temporal work-case 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
$\rho $
-Algorithm by deploying the implemented
$\rho $
-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.