Discrete-time signal processing
Discrete-time signal processing
Introduction to Automata Theory, Languages and Computability
Introduction to Automata Theory, Languages and Computability
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
A Probabilistic XML Approach to Data Integration
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
IT support for healthcare processes - premises, challenges, perspectives
Data & Knowledge Engineering
Monitoring Dependencies for SLAs: The MoDe4SLA Approach
SCC '08 Proceedings of the 2008 IEEE International Conference on Services Computing - Volume 1
Value-Driven Coordination Process Design Using Physical Delivery Models
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
Building Scientific Workflow with Taverna and BPEL: A Comparative Study in caGrid
Service-Oriented Computing --- ICSOC 2008 Workshops
Scientific Workflows: Business as Usual?
BPM '09 Proceedings of the 7th International Conference on Business Process Management
Experiment selection for the discrimination of semi-quantitative models of dynamical systems
Artificial Intelligence
Model identification in interactive influence diagrams using mutual information
Web Intelligence and Agent Systems
Consistency between e3-value models and activity diagrams in a multi-perspective development method
OTM'05 Proceedings of the 2005 Confederated international conference on On the Move to Meaningful Internet Systems - Volume >Part I
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Some physical objects are influenced by business workflows and are observed by sensors. Since both sensor infrastructures and business workflows must deal with imprecise information, the correlation of sensor data and business workflow data related to physical objects might be used a-posteriori to determine the source of the imprecision. In this paper, an information theory based approach is presented to distinguish sensor infrastructure errors from inhomogeneous business workflows. This approach can be applied on detecting imprecisions in the sensor infrastructure, like e.g. sensor errors or changes of the sensor infrastructure deployment.