Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Average reward reinforcement learning: foundations, algorithms, and empirical results
Machine Learning - Special issue on reinforcement learning
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Temporal causal modeling with graphical granger methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Exploring event correlation for failure prediction in coalitions of clusters
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Event queries on correlated probabilistic streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Exploiting Lineage for Confidence Computation in Uncertain and Probabilistic Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
ProActive Caching - A Framework for Performance Optimized Access Control Evaluations
POLICY '09 Proceedings of the 2009 IEEE International Symposium on Policies for Distributed Systems and Networks
Efficient solution algorithms for factored MDPs
Journal of Artificial Intelligence Research
Cp-logic: A language of causal probabilistic events and its relation to logic programming
Theory and Practice of Logic Programming
Discovering actionable patterns in event data
IBM Systems Journal
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Context aware computing and its utilization in event-based systems
Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems
Proactive SLA Negotiation for Service Based Systems
SERVICES '10 Proceedings of the 2010 6th World Congress on Services
Event Processing in Action
Recognizing patterns in streams with imprecise timestamps
Proceedings of the VLDB Endowment
RFID Authentication Efficient Proactive Information Security within Computational Security
Theory of Computing Systems
Intrusion detection using continuous time Bayesian networks
Journal of Artificial Intelligence Research
Continuous time bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Efficient Processing of Uncertain Events in Rule-Based Systems
IEEE Transactions on Knowledge and Data Engineering
Future internet technology for the future of transport and logistics
ServiceWave'11 Proceedings of the 4th European conference on Towards a service-based internet
An adaptive event stream processing environment
PhD '12 Proceedings of the on SIGMOD/PODS 2012 PhD Symposium
A basic model for proactive event-driven computing
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
Information system monitoring and notifications using complex event processing
Proceedings of the Fifth Balkan Conference in Informatics
Proceedings of the 7th ACM international conference on Distributed event-based systems
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Event driven architecture is a paradigm shift from traditional computing architectures which employ synchronous, request-response interactions. In this paper we introduce a conceptual architecture for what can be considered the next phase of that evolution: proactive event-driven computing. Proactivity refers to the ability to mitigate or eliminate undesired future events, or to identify and take advantage of future opportunities, by applying prediction and automated decision making technologies. We investigate an extension of the event processing conceptual model and architecture to support proactive event-driven applications, and propose the main building blocks of a novel architecture. We first describe several extensions to the existing event processing functionality that is required to support proactivity; next, we extend the event processing agent model to include two more type of agents: predictive agents that may derive future uncertain events based on prediction models, and proactive agents that compute the best proactive action that should be taken. Those building blocks are demonstrated through a comprehensive scenario that deals with proactive decision making, ensuring timely delivery of critical material for a production plant.