IEEE Transactions on Computers
Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
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
Querying Imprecise Data in Moving Object Environments
IEEE Transactions on Knowledge and Data Engineering
SEA-CNN: Scalable Processing of Continuous K-Nearest Neighbor Queries in Spatio-temporal Databases
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Adaptive stream filters for entity-based queries with non-value tolerance
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Energy-efficient monitoring of extreme values in sensor networks
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Energy-Efficient Monitoring of Mobile Objects with Uncertainty-Aware Tolerances
IDEAS '07 Proceedings of the 11th International Database Engineering and Applications Symposium
Monochromatic and bichromatic reverse skyline search over uncertain databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A query processor for prediction-based monitoring of data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Adaptive Safe Regions for Continuous Spatial Queries over Moving Objects
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Efficient Construction of Compact Shedding Filters for Data Stream Processing
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Spatial Range Querying for Gaussian-Based Imprecise Query Objects
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Minimizing Communication Cost in Distributed Multi-query Processing
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Minimizing the communication cost for continuous skyline maintenance
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
The VLDB Journal — The International Journal on Very Large Data Bases
Online piece-wise linear approximation of numerical streams with precision guarantees
Proceedings of the VLDB Endowment
Energy efficient monitoring in sensor networks
LATIN'08 Proceedings of the 8th Latin American conference on Theoretical informatics
A probabilistic filter protocol for continuous queries
QuaCon'09 Proceedings of the 1st international conference on Quality of context
Probabilistic filters: A stream protocol for continuous probabilistic queries
Information Systems
A filter-based protocol for continuous queries over imprecise location data
Proceedings of the 21st ACM international conference on Information and knowledge management
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Pervasive applications, such as natural habitat monitoring and location-based services, have attracted plenty of research interest. These applications deploy a large number of sensors (e.g. temperature sensors) and positioning devices (e.g. GPS) to collect data from external environments. Very often, these systems have limited network bandwidth and battery resources. The sensors also cannot record accurate values. The uncertainty of these data hence has to been taken into account for query evaluation purposes. In particular, probabilistic queries, which consider data impreciseness and provide statistical guarantees in answers, have been recently studied. In this paper, we investigate how to evaluate a long-standing (or continuous) probabilistic query. We propose the probabilistic filter protocol, which governs remote sensor devices to decide upon whether values collected should be reported to the query server. This protocol effectively reduces the communication and energy costs of sensor devices. We also introduce the concept of probabilistic tolerance, which allows a query user to relax answer accuracy, in order to further reduce the utilization of resources. Extensive simulations on realistic data show that our method reduces by address more than 99% of savings in communication costs.