Handling Big(ger) logs: Connecting ProM 6 to apache hadoop

Hernández, S. (Universidad de Zaragoza) ; Van Zelst, S.J. ; Ezpeleta, J. (Universidad de Zaragoza) ; Van der Aalst, W.M.P.

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: 0.195 - Computer Science (miscellaneous)

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): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)