Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems
The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Machine Learning
High-performance complex event processing over streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Trio: a system for data, uncertainty, and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Management of probabilistic data: foundations and challenges
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient query evaluation on probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient pattern matching over event streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Efficient Complex Event Processing over RFID Data Stream
ICIS '08 Proceedings of the Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008)
Plan-based complex event detection across distributed sources
Proceedings of the VLDB Endowment
A Novel Distributed Complex Event Processing for RFID Application
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 01
OpenStreetMap: User-Generated Street Maps
IEEE Pervasive Computing
Event-processing network model and implementation
IBM Systems Journal
Recognizing activities with multiple cues
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Complex Event Detection in Probabilistic Stream
APWEB '10 Proceedings of the 2010 12th International Asia-Pacific Web Conference
Event Processing in Action
Complex Event Processing over Uncertain Data Streams
3PGCIC '10 Proceedings of the 2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing
Recognizing patterns in streams with imprecise timestamps
Proceedings of the VLDB Endowment
On the implementation of the probabilistic logic programming language problog
Theory and Practice of Logic Programming
Bridging physical and virtual worlds: complex event processing for RFID data streams
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Multi-agent event recognition in structured scenarios
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Event processing under uncertainty
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
Location-based reasoning about complex multi-agent behavior
Journal of Artificial Intelligence Research
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With the rapid development of Internet of Things (IoT), enormous events are produced every day. Complex Event Processing (CEP), which can be used to extract high level patterns from raw data, becomes the key part of the IoT middleware. In large-scale IoT applications, the current CEP technology encounters the challenge of massive distributed data which cannot be handled by most of the current methods efficiently. Another challenge is the uncertainty of the data caused by noise, sensor error or wireless communication techniques. In order to solve these challenges, in this paper a high-performance complex event processing method over distributed probabilistic event streams is proposed. With the ability to report confidence for processed complex events over uncertain data, this method uses probabilistic nondeterministic finite automaton and active instance stacks to process a complex event in both single and distributed probabilistic event streams. A parallel algorithm is designed to improve the performance. A query plan-based method is used to process the hierarchical complex event from distributed event streams. Query plan optimization is proposed based on the query optimization technology of probabilistic databases. The experimental study shows that this method is efficient in processing complex events over distributed probabilistic event streams.