Conformance Checking and Diagnosis in Process Mining: by Jorge Munoz-Gama

By Jorge Munoz-Gama

Process mining thoughts can be utilized to find, learn and enhance genuine procedures, via extracting versions from saw habit. the purpose of this ebook is conformance checking, one of many major components of procedure mining. In conformance checking, latest technique versions are in comparison with real observations of the method which will investigate their caliber. Conformance checking recommendations are the way to visualize the diversities among assumed strategy represented within the version and the genuine approach within the occasion log, pinpointing attainable difficulties to handle, and the company method administration effects that depend upon those models.
This booklet combines either software and learn views. It offers concrete use situations that illustrate the issues addressed by means of the recommendations within the publication, yet while, it comprises entire conceptualization and formalization of the matter and the options, and during reviews at the caliber and the functionality of the proposed thoughts. consequently, this e-book brings the chance for enterprise analysts keen to enhance their association approaches, and likewise info scientists relating to process-oriented facts science.

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Additional resources for Conformance Checking and Diagnosis in Process Mining: Comparing Observed and Modeled Processes (Lecture Notes in Business Information Processing)

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Although this can be useful to get insights on the precision from a high level, it becomes a limitation when it comes to detect exactly those precision problems. Moreover, the correct categorization of follows/precedes relations is not guaranteed when the traces contain loops [73]. Finally, building the relations from a model point of view requires the analysis of the model task sequences based on a state space analysis or an exhaustive model simulation. This limits the applicability of the approach to examples of low complexity, or forces the use of approximations to alleviate the complexity.

The numbers are weighted according to the importance of the state. Formally: etc p (ALM ) = 1 − ∑σ ∈SLM ω(σ ) · |esc(σ )| ∑σ ∈SLM ω(σ ) · |mod(σ )| Let us consider, for example, the automaton AL1 SN 1 . The automaton contains 21 states in SLM , denoted in white. For each one of those states we compute the number of escaping arcs and the number of modeled arcs, and we weight them according to the weight of each state. 81, denoting a moderate precision degree of 81%. Taking a look at the literature one can see that the intuitive notion of precision is difficult to capture in a metric.

55] defines a metric –behavioral precision– to measure the precision between two models and a log, evaluating how much of the first model behavior is covered by the second. This measure is used within the Genetic Miner [56, 54] –a discovery 36 4 Precision in Conformance Checking approach based on evolutionary algorithms– to evaluate the quality of the population obtained. Goedertier et al. [45] introduces the use of artificial negative examples to measure the precision between an event log and a process model.

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