Plataforma para la agrupación de modelos de procesos de negocio bajo el algoritmo de clustering K-MENS - Valencia Bacca, Felipe Alejandro (Universidad de San Buenaventura - Cali, 2017)
The purpose of this paper is to construct a web platform with a scalable architecture, which performs multimodal searches in business process models in order to perform groupings through the algorithm of Clustering K-MEANS, which aims to partition into groups (k) a set of observations (n), applying it to business process models. The platform uses a repository of business process models in XPDL format, which evaluates the grouping of these by means of internal metrics, which do not need human intervention. Nowadays companies that have business process models see themselves in the arduous task of searching on repositories with very large volumes when it is necessary to make modifications or reuses, this demands time, generates reprocessing and costs. The initiative is to develop a web platform with a centralized repository, where you can perform the search and grouping of business process models, with an automatic search grouping strategy, presenting the search results in an orderly and categorized way allowing The user spends less time and can perform a more effective analysis. The platform was developed under a methodology of development in cascade, with the advice and the accompaniment of the Ing. Hugo Ordoñez, PhD., and together with the students Dilan Mejía and Santiago Rodríguez.