Snoop: an expressive event specification language for active databases
Data & Knowledge Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Machine Learning
The CQL continuous query language: semantic foundations and query execution
The VLDB Journal — The International Journal on Very Large Data Bases
Event queries on correlated probabilistic streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Complex event processing over uncertain data
Proceedings of the second international conference on Distributed event-based systems
Tuning complex event processing rules using the prediction-correction paradigm
Proceedings of the Third ACM International Conference on Distributed Event-Based Systems
Event-Driven Approach for Logic-Based Complex Event Processing
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 01
Rule-based composite event queries: the language XChangeEQ and its semantics
RR'07 Proceedings of the 1st international conference on Web reasoning and rule systems
TESLA: a formally defined event specification language
Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems
Towards proactive event-driven computing
Proceedings of the 5th ACM international conference on Distributed event-based system
Towards an inexact semantic complex event processing framework
Proceedings of the 5th ACM international conference on Distributed event-based system
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With the increasing application of Event Stream Processing (ESP) for event pattern detection, it has become important to enhance the extant ESP capabilities to deal with applications having dynamic behavior. This dissertation research explores the limitations of current ESP systems due to fixed pattern detection mechanism and discusses the motivational ideas that demand enhancements in ESP. We propose a solution called adaptive ESP that explores, learns, and updates evolving patterns in dynamic applications. Development of adaptive ESP requires several research issues to be addressed: such as handling input data streams, enhancing event languages with probabilistic information, using machine learning algorithms, and processing feedback from experts. We discuss these issues with the proposed architecture for the system and explore research issues and some of the initial work for developing adaptive ESP.