Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Entropy and information theory
Entropy and information theory
Elements of information theory
Elements of information theory
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
An Introduction to Variational Methods for Graphical Models
Machine Learning
Selectivity estimation using probabilistic models
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
A Bayesian metareasoner for algorithm selection for real-time Bayesian network inference problems
Eighteenth national conference on Artificial intelligence
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Adaptive filters for continuous queries over distributed data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
A probabilistic approach to inference with limited information in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Adaptive stream resource management using Kalman Filters
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Exploiting Correlated Attributes in Acquisitional Query Processing
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Update rules for parameter estimation in Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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As large-scale sensor networks are being deployed with the objective of collecting quality data to support user queries and decision-making, the role of a scalable query model becomes increasingly critical. An effective query model should scale well with large network deployments and address user queries at specified confidence while maximizing sensor resource conservation. In this paper, we propose a group-query processing scheme using Bayesian Networks (BNs). When multiple sensors are queried, the queries can be processed collectively as a single group-query that exploits inter-attribute dependencies for deriving cost-effective query plans. We show that by taking advantage of the Markov-blanket property of BNs, we can generate resource-conserving group-query plans, and also address a new class of diagnostic queries. Through empirical studies on synthetic and real-world datasets, we show the effectiveness of our scheme over existing correlation-based models.