Operation Record Based Workflow Extracting Method for Personal Information Management System
APCHI '98 Proceedings of the Third Asian Pacific Computer and Human Interaction
Organizational Data Mining: Leveraging Enterprise Data Resources for Optimal Performance
Organizational Data Mining: Leveraging Enterprise Data Resources for Optimal Performance
Evaluating Flexible Workflow Systems
HICSS '97 Proceedings of the 30th Hawaii International Conference on System Sciences: Information Systems Track-Collaboration Systems and Technology - Volume 2
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Fractal architecture for the adaptive complex enterprise
Communications of the ACM - Adaptive complex enterprises
A Business Activity Monitoring System Supporting Real-Time Business Performance Management
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 01
Amoeba: A methodology for modeling and evolving cross-organizational business processes
ACM Transactions on Software Engineering and Methodology (TOSEM)
Achieving 'handoff' traceability for complex systemimprovement
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
Web Services Research for Emerging Applications: Discoveries and Trends
Web Services Research for Emerging Applications: Discoveries and Trends
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Complex service-oriented organizations such as IT customer service or the hospital emergency deal with many challenges due to incoming request types that we characterize as non-routine. Each such request reflects significant variations in the environment and consequently requirements, which drives discovery of processing needs. At the same time such organizations are often challenged with sharing high-cost resources and satisfying multiple stakeholders with different expectations. Performance improvement in this context is particularly challenging and requires new methods. To address this, the authors present an ontology designed for highly dynamic service organizations where traceable workflow data is difficult to obtain and there are many stakeholders. The ontology provides the contextual framework by with useful knowledge can be successfully extracted from mined performance data obtained from scattered sources. Specifically the service ontology 1 obtains tacit knowledge as explicit in-the-micro feedback from workers performing Roles, 2 provides the structure for organizing in-the-small execution data from evolving process and instances, and 3 aggregates process instances metrics into a performance and decision-making facility to align to in-the-large goals of stakeholders. Using actual customer service requests they illustrate the benefits of the ontology for relating aggregated goals to feedback from individual roles of workers. The authors also illustrate the benefits in terms of identifying actionable improvement targets.