Principles of distributed database systems (2nd ed.)
Principles of distributed database systems (2nd ed.)
Adaptive precision setting for cached approximate values
SIGMOD '01 Proceedings of the 2001 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
Taming the underlying challenges of reliable multihop routing in sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Convex Optimization
Adaptive stream resource management using Kalman Filters
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
REED: robust, efficient filtering and event detection in sensor networks
VLDB '05 Proceedings of the 31st international conference on Very large data bases
On In-network Synopsis Join Processing for Sensor Networks
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
In-network execution of monitoring queries in sensor networks
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Retrieval of Spatial Join Pattern Instances from Sensor Networks
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
A Distributed Algorithm for Joins in Sensor Networks
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
STAR: self-tuning aggregation for scalable monitoring
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Towards Efficient Processing of General-Purpose Joins in Sensor Networks
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
On Join Location in Sensor Networks
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
Join of Multiple Data Streams in Sensor Networks
IEEE Transactions on Knowledge and Data Engineering
Communication-Efficient implementation of range-joins in sensor networks
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Communication-Efficient implementation of join in sensor networks
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Top-k query evaluation in sensor networks with the guaranteed accuracy of query results
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
A continuous query evaluation scheme for a detection-only query over data streams
Proceedings of the 20th ACM international conference on Information and knowledge management
TWINS: Efficient time-windowed in-network joins for sensor networks
Information Sciences: an International Journal
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While join processing in wireless sensor networks has received a lot of attention recently, current solutions do not work well for continuous queries. In those networks however, continuous queries are the rule. To minimize the communication costs of join processing, it is important to not ship non-joining tuples. In order to know which tuples do not join, prior work has proposed a precomputation step. For continuous queries however, repeating the precomputation for each execution is unnecessary and leaves aside that data tends to be temporally correlated. In this paper, we present a filtering approach for the processing of continuous join queries. We propose to keep the filters and to maintain them. The problems are determining the sizes of the filters and deciding which filters to update. Simplistic approaches result in bad performance. We show how to compute solutions that are optimal. Experiments on real-world sensor data indicate that our method performs close to a theoretical optimum and consistently outperforms state-of-the-art join approaches.