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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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. 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. 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. 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. Moreover, using RapidProM, one can benefit from combinations of process mining with other types of analysis available through the RapidMiner marketplace.
Complex process mining workflows can be modeled and executed easily and subsequently reused for other data sets. RapidProM, an extension of RapidMiner based on ProM, combines the best of both worlds.
However, ProM does not support analysis based on scientific workflows. ProM is a powerful open-source process mining tool supporting hundreds of analysis techniques. Process mining is very different from conventional data mining and machine learning techniques. Process mining helps to turn these data into real value: by discovering the real processes, by automatically identifying bottlenecks, by analyzing deviations and sources of non-compliance, by revealing the actual behavior of people, etc. Event data are a valuable source of information for organizations that need to meet requirements related to compliance, efficiency, and customer service. This applies to all domains: from health care and e-government to production and maintenance. The number of events recorded for operational processes is growing every year.
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