Abstract. When using process models for automation, correctness of the models is a key requirement. While many approaches concentrate on control flow verification only, correct data flow modeling is of similar importance. This paper introduces an approach for detecting and repairing modeling errors

Network anomalies occur in operational networks and may be logged by a number of network measurement tools such as SNMP and NetFlow. However, accurate and efficient detection of these anomalies in the logged data is very challenging due to the huge data volume and complex characteristics

this paper has already identified a number of problems with existing systems

Abstract not found

Ontology population is prone to cause inconsistency because the populating process is imprecise or the populated data may conflict with the original data. By assuming that the intensional part of the populated DL-based ontology is fixed and each removable ABox assertion is given a removal cost, we

Anomalous data lead to scientific discoveries. Although machine learning systems can be forced to resolve anomalous data, these systems use general learning algorithms to do so. To determine whether anomaly-driven approaches to discovery produce more accurate models than the standard approaches, we

anomalies

quality control (QC) that exploits the spatial and temporal correlations in the data to distinguish sensor failures from valid observations. The model adapts to each deployment site by learning a Bayesian network structure that captures spatial relationships between sensors, and it extends the structure

Ontology population is prone to cause inconsistency because the populating process is imprecise or the populated data may conflict with the original data. By assuming that the intensional part of the populated DL-based ontology is fixed and each removable ABox assertion is given a removal cost, we

Characterization of diagnosability and repairability for