The HiPAC project: combining active databases and timing constraints
ACM SIGMOD Record - Special Issue on Real-Time Database Systems
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Active Rules in Database Systems
Active Rules in Database Systems
Active Database Systems: Triggers and Rules for Advanced Database Processing
Active Database Systems: Triggers and Rules for Advanced Database Processing
Composite Event Specification in Active Databases: Model & Implementation
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
On the Semantics of Complex Events in Active Database Management Systems
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Reasoning about Uncertainty
The VLDB Journal — The International Journal on Very Large Data Bases
Approximate Data Collection in Sensor Networks using Probabilistic Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Epi-SPIRE: a system for environmental and public health activity monitoring
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Query-based monitoring of BPEL business processes
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Towards expressive publish/subscribe systems
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Efficient uncertainty management in complex event systems: saving the witch from Henzel & Gretel
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
Predictive publish/subscribe matching
Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems
Enriching events to support hospital care
Proceedings of the 7th Middleware Doctoral Symposium
Event modelling and reasoning with uncertain information for distributed sensor networks
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Processing flows of information: From data stream to complex event processing
ACM Computing Surveys (CSUR)
An adaptive event stream processing environment
PhD '12 Proceedings of the on SIGMOD/PODS 2012 PhD Symposium
Approximate semantic matching of heterogeneous events
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
A methodology for designing events and patterns in fast data processing
CAiSE'13 Proceedings of the 25th international conference on Advanced Information Systems Engineering
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In recent years, there has been a growing need for active systems that can react automatically to events. Some events are generated externally and deliver data across distributed systems, while others are materialized by the active system itself. Event materialization is hampered by uncertainty that may be attributed to unreliable data sources and networks, or the inability to determine with certainty whether an event has actually occurred. Two main obstacles exist when designing a solution to the problem of event materialization with uncertainty. First, event materialization should be performed efficiently, at times under a heavy load of incoming events from various sources. The second challenge involves the generation of a correct probability space, given uncertain events. We present a solution to both problems by introducing an efficient mechanism for event materialization under uncertainty. A model for representing materialized events is presented and two algorithms for correctly specifying the probability space of an event history are given. The first provides an accurate, albeit expensive method based on the construction of a Bayesian network. The second is a Monte Carlo sampling algorithm that heuristically assesses materialized event probabilities. We experimented with both the Bayesian network and the sampling algorithms, showing the latter to be scalable under an increasing rate of explicit event delivery and an increasing number of uncertain rules (while the former is not). Finally, our sampling algorithm accurately and efficiently estimates the probability space.