Monitoring and analyzing influential factors of business process performance

  • Authors:

  • Branimir Wetzstein;Philipp Leitner;Florian Rosenberg;Ivona Brandic;Schahram Dustdar;Frank Leymann

  • Affiliations:

  • Institute of Architecture of Application Systems, University of Stuttgart, Stuttgart, Germany;Distributed Systems Group, Vienna University of Technology, Vienna, Austria;Distributed Systems Group, Vienna University of Technology, Vienna, Austria;Distributed Systems Group, Vienna University of Technology, Vienna, Austria;Distributed Systems Group, Vienna University of Technology, Vienna, Austria;Institute of Architecture of Application Systems, University of Stuttgart, Stuttgart, Germany

  • Venue:
  • EDOC'09 Proceedings of the 13th IEEE international conference on Enterprise Distributed Object Computing
  • Year:
  • 2009

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Abstract

Business activity monitoring enables continuous observation of key performance indicators (KPIs). However, if things go wrong, a deeper analysis of process performance becomes necessary. Business analysts want to learn about the factors that influence the performance of business processes and most often contribute to the violation of KPI target values, and how they relate to each other. We provide a framework for performance monitoring and analysis of WS-BPEL processes, which consolidates process events and Quality of Service measurements. The framework uses machine learning techniques in order to construct tree structures, which represent the dependencies of a KPI on process and QoS metrics. These dependency trees allow business analysts to analyze how the process KPIs depend on lower-level process metrics and QoS characterisitics of the IT infrastructure. Deeper knowledge about the structure of dependencies can be gained by drill-down analysis of single factors of influence.