Gigascope: a stream database for network applications
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Anomaly Detection Using Real-Valued Negative Selection
Genetic Programming and Evolvable Machines
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient elastic burst detection in data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Approximations to Magic: Finding Unusual Medical Time Series
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
ACM Computing Surveys (CSUR)
XStream: a Signal-Oriented Data Stream Management System
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Adaptive system anomaly prediction for large-scale hosting infrastructures
Proceedings of the 29th ACM SIGACT-SIGOPS symposium on Principles of distributed computing
Active complex event processing: applications in real-time health care
Proceedings of the VLDB Endowment
| Hi-index | 0.00 |
An approach is developed where functions are used in a data stream management system to continuously validate data streaming from industrial equipment based on mathematical models of the expected behavior of the equipment. The models are expressed declaratively using a data stream query language. To validate and detect abnormality in data streams, a model can be defined either as an analytical model in terms of functions over sensor measurements or be based on learning a statistical model of the expected behavior of the streams during training sessions. It is shown how parallel data stream processing enables equipment validation based on expensive models while scaling the number of sensor streams without causing increasing delays. The paper presents two demonstrators based on industrial cases and scenarios where the approach has been implemented.