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El autor David Chapela-Campa ha publicado 4 artículo(s):

1 - Towards the Extraction of Frequent Patterns in Complex Process Models

In this paper, we present WoMine, an algorithm to retrieve frequent behavioural patterns from the model. Our approach searches in process models extracting structures with sequences, selections, parallels and loops, which are frequently executed in the logs. This proposal has been validated with a set of process models, and compared with the state of the art techniques. Experiments have validated that WoMine can find all types of patterns, extracting information that cannot be mined with the state of the art techniques.

Autores: David Chapela-Campa / Manuel Mucientes / Manuel Lama / 
Palabras Clave: frequent pattern mining - process discovery - Process Mining

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3 - Discovering Infrequent Behavioral Patterns in Process Models (Summary)

In this paper we present WoMine-i, a novel algorithm to detect infrequent behavioural patterns from a process model, measuring their frequency with the instances of the log. A behavioural pattern is a subgraph of the process model, com- posed by all type of structures —sequences, selections, parallels and/or loops—, which represents the behaviour of a part of the process. And it is considered infrequent when its complete execution happens in a number of cases from the log below a predefined threshold. To find the infrequent patterns, WoMine-i performs an a priori search starting with the minimal structures of the model. In this search, there is an expansion stage done in two ways: i) adding other minimal structures not contained in the current pattern, and ii) adding arcs. This expansion is followed by a pruning strategy that verifies the upward-closure property of support —also known as monotonicity. This property ensures that if a pattern is infrequent, all patterns containing it will be infrequent and, thus, it is no necessary to continue expanding it —the minimum pattern itself expresses all the infrequent behaviour containing it. This pruning presents an exception in order to simplify the results: If a pattern is infrequent and maintains the value of its frequency with the expansion, it is not removed from the expansion stage —it means it is being expanded with a selection branch with less frequency. In this way, WoMine-i returns the largest patterns expressing the minimum infrequent behaviour. In each step of the iterative process, WoMine-i reduces the search space by pruning some of the generated patterns. For this, an algorithm to check the frequency of a pattern is needed. WoMine-i generates the different paths of a pattern, henceforth simple patterns, and checks the frequency of each one. To measure the frequency of a simple pattern, WoMine-i replays the traces of the log and checks how many of them are compliant with it. At each step of the replay, the algorithm checks if the simple pattern is being correctly executed. Afterwards, WoMine-i assigns to the pattern the higher of its simple patterns frequency, and checks if the pattern is considered frequent w.r.t. the threshold. Thereby, given a log and a corresponding model, WoMine-i is able to return the infrequent patterns of it. Allowing to study them and enhance the process consequently.

Autores: David Chapela-Campa / Manuel Mucientes / Manuel Lama / 
Palabras Clave:

4 - Pattern-based Simplification of Process Models

Several simplification techniques have been proposed to deal with the understanding of complex process models, from the structural simplification of the model to the simplification of the log to discover simpler process models. But obtaining a comprehensible model explaining the behaviour of unstructured large processes is still an open challenge. In this paper, we present a novel algorithm to simplify process models by abstracting the infrequent behaviour in the logs.

Autores: David Chapela-Campa / Manuel Mucientes / Manuel Lama / 
Palabras Clave: event abstraction - model simplification - Process Mining