The Asilomar report on database research
ACM SIGMOD Record
Production workflow: concepts and techniques
Production workflow: concepts and techniques
Improving Business Process Quality through Exception Understanding, Prediction, and Prevention
Proceedings of the 27th International Conference on Very Large Data Bases
Framework for Semantic Web Process Composition
International Journal of Electronic Commerce
Process Mining towards Semantics
Advances in Web Semantics I
An outlook on semantic business process mining and monitoring
OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems - Volume Part II
In situ evaluation of recommender systems: Framework and instrumentation
International Journal of Human-Computer Studies
Automated management of assets based on RFID triggered alarm messages
Information Systems Frontiers
Applying process mining in SOA environments
ICSOC/ServiceWave'09 Proceedings of the 2009 international conference on Service-oriented computing
Meronymy-based aggregation of activities in business process models
ER'10 Proceedings of the 29th international conference on Conceptual modeling
A semantic approach for business process model abstraction
CAiSE'11 Proceedings of the 23rd international conference on Advanced information systems engineering
Process management in health care: a system for preventing risks and medical errors
BPM'05 Proceedings of the 3rd international conference on Business Process Management
From fine-grained to abstract process models: A semantic approach
Information Systems
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Business Process Management Systems log a large amount of operational data about processes and about the (human and automated) resources involved in their executions. This information can be analyzed for assessing the quality of business operations, identify problems, and suggest solutions. However, current process analysis systems lack the functionalities required to provide information that can be immediately digested and used by business analysts to take decisions. In this paper we discuss the limitations of existing approaches and we present a system and a set of techniques, developed at Hewlett-Packard, that overcome this limitations, enabling the use of log data for efficient business-level analysis of business processes.