Introduction to Algorithms
Offering a Precision-Performance Tradeoff for Aggregation Queries over Replicated Data
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
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
Approximate Selection Queries over Imprecise Data
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Exploiting Correlated Attributes in Acquisitional Query Processing
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Change Tolerant Indexing for Constantly Evolving Data
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Cost-efficient processing of MIN/MAX queries over distributed sensors with uncertainty
Proceedings of the 2005 ACM symposium on Applied computing
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Approximate Data Collection in Sensor Networks using Probabilistic Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Range search on multidimensional uncertain data
ACM Transactions on Database Systems (TODS)
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient indexing methods for probabilistic threshold queries over uncertain data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Probabilistic Verifiers: Evaluating Constrained Nearest-Neighbor Queries over Uncertain Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Selective data acquisition for probabilistic K-NN query
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Data selection for exact value acquisition to improve uncertain clustering
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Explore or exploit?: effective strategies for disambiguating large databases
Proceedings of the VLDB Endowment
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In applications like sensor network monitoring and location-based services, due to limited network bandwidth and battery power, a system cannot always acquire accurate and fresh data from the external environment. To capture data errors in these environments, recent researches have proposed to model uncertainty as a probability distribution function (pdf), as well as the notion of probabilistic queries, which provide statistical guarantees on answer correctness. In this paper, we present an entropy-based metric to quantify the degree of ambiguity of probabilistic query answers due to data uncertainty. Based on this metric, we develop a new method to improve the query answer quality. The main idea of this method is to acquire (or probe) data from a selected set of sensing devices, in order to reduce data uncertainty and improve the quality of a query answer. Given that a query is assigned a limited number of probing resources, we investigate how the quality of a query answer can attain an optimal improvement. To improve the efficiency of our solution, we further present heuristics which achieve near-to-optimal quality improvement. We generalize our solution to handle multiple queries. An experimental simulation over a realistic dataset is performed to validate our approaches.