Runtime prediction of service level agreement violations for composite services
ICSOC/ServiceWave'09 Proceedings of the 2009 international conference on Service-oriented computing
Event driven monitoring for service composition infrastructures
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Constraint-Based runtime prediction of SLA violations in service orchestrations
ICSOC'11 Proceedings of the 9th international conference on Service-Oriented Computing
An operational decision support framework for monitoring business constraints
FASE'12 Proceedings of the 15th international conference on Fundamental Approaches to Software Engineering
Incremental service level agreements violation handling with time impact analysis
Journal of Systems and Software
Improving business process decision making based on past experience
Decision Support Systems
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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.