Handling Big(ger) logs: Connecting ProM 6 to apache hadoop
Resumen: Within process mining the main goal is to support the analysis, im- provement and apprehension of business processes. Numerous process mining techniques have been developed with that purpose. The majority of these tech- niques use conventional computation models and do not apply novel scalable and distributed techniques. In this paper we present an integrative framework connect- ing the process mining framework ProM with the distributed computing environ- ment Apache Hadoop. The integration allows for the execution of MapReduce jobs on any Apache Hadoop cluster enabling practitioners and researchers to ex- plore and develop scalable and distributed process mining approaches. Thus, the new approach enables the application of different process mining techniques to events logs of several hundreds of gigabytes.
Idioma: Inglés
Año: 2015
Publicado en: CEUR Workshop Proceedings 1418 (2015), 80-84
ISSN: 1613-0073

Originalmente disponible en: Texto completo de la revista

Factor impacto SCIMAGO:

Financiación: info:eu-repo/grantAgreement/ES/DGA/87230-2
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TIN2014-56633-C3-2-R
Tipo y forma: Article (Published version)
Área (Departamento): Lenguajes y Sistemas Informáticos (Departamento de Informática e Ingeniería de Sistemas)

Creative Commons You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.


Exportado de SIDERAL (2018-04-10-12:17:44)

Este artículo se encuentra en las siguientes colecciones:
Articles > Artículos por área > Lenguajes y Sistemas Informáticos



 Record created 2016-01-22, last modified 2018-04-10


Versión publicada:
 PDF
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)