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In this paper is analyzed and modeled the problem of discovery process models, in order to apply metaheuristic algorithms based on a point and populations. There are two main reasons for the growing interest to improving the efficiency of the discovery algorithms in process mining. First or all, the information systems, that support business processes in organizations, record the details of the executions. These contain a potential knowledge for decision making. In addition to the growing need to support business processes in competitive and rapidly changing environments. In order to meet the objective of this work that is selected best suited metaheuristics algorithms to the problem: The Hill Climbing and Evolutionary Strategy. The main contribution consists in modeling a first approach to the problem of finding models using metaheuristic strategies. [ABSTRACT FROM AUTHOR]

The work is based on the proposed clustering model to support decision making based on analysis of records in the execution of processes in organizations that implement BPM technologies. An investigation is conducted that includes the study of how to integrate a model based on a data mining algorithm to business processes and how, from the results of the implementation of processes, can generate knowledge to improve performance indicators in their implementation. The proposal includes the creation of a tool based on the implementation of the clustering technique of data mining to find useful knowledge. It is developed using open source technologies. [ABSTRACT FROM AUTHOR]

The Notations and languages for modelling processes have been applied for years in many fields of industry. However, the uses of them are relatively recent in the field of Software Engineering. The objective of this paper is to present the proposed improvements in the capture of requirements using BPA techniques. Their contribution to facilitate the decision making process in the description of the modelling business device. [ABSTRACT FROM AUTHOR]