Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Dimensions: why do we need a new data handling architecture for sensor networks?
ACM SIGCOMM Computer Communication Review
Research issues in data stream association rule mining
ACM SIGMOD Record
A Pipelined Framework for Online Cleaning of Sensor Data Streams
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
On optimal communication cost for gathering correlated data through wireless sensor networks
Proceedings of the 12th annual international conference on Mobile computing and networking
Online outlier detection in sensor data using non-parametric models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Entirely declarative sensor network systems
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
A spatial sampling scheme based on innovations diffusion in sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Robust management of outliers in sensor network aggregate queries
MobiDE '07 Proceedings of the 6th ACM international workshop on Data engineering for wireless and mobile access
Exploiting data correlation for multi-scale processing in sensor networks
Proceedings of the 2nd international conference on Scalable information systems
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Recent years of research on sensor networks have resulted in multi-scale processing techniques for sensor data mining able to reflect the dynamic nature of real-world context. However, few of these techniques provide a systematic view of the relationships between sensor data streams and correlated network behaviors. In this paper, an association model of inherent, data and network properties is presented and analyzed for a suite of event diffusion spotting applications. Based on the associated model, window-based in-network cooperation is conducted for sensitive event diffusion spotting. Experimental results verify the performance of our approach, and confirm the scalability of our association perspective of sensor properties in such event diffusion spotting networks.