The problem of representing and querying sensor-network data issues new research challenges, as traditional techniques and architectures used for managing relational and object oriented databases are not suitable in this context. In this paper we present a Grid-based architecture that supports aggregate query answering on sensor network data, and uses a summarization technique to efficiently accomplish this task. In particular, grid nodes are used either to collect, compress and store sensor readings, and to extract information from stored data. Grid nodes can exchange information among each other, so that the same piece of information can be stored (with a different degree of accuracy) into several nodes. Queries are evaluated by locating the grid nodes containing the needed information, and choosing (among these nodes) the most convenient ones, according to a cost model. © Springer-Verlag 2004.

A Grid Framework for Approximate Aggregate Query Answering on Summarized Sensor Network Readings

FILIPPO FURFARO;SACCA', Domenico;CUZZOCREA, Alfredo Massimiliano
2004-01-01

Abstract

The problem of representing and querying sensor-network data issues new research challenges, as traditional techniques and architectures used for managing relational and object oriented databases are not suitable in this context. In this paper we present a Grid-based architecture that supports aggregate query answering on sensor network data, and uses a summarization technique to efficiently accomplish this task. In particular, grid nodes are used either to collect, compress and store sensor readings, and to extract information from stored data. Grid nodes can exchange information among each other, so that the same piece of information can be stored (with a different degree of accuracy) into several nodes. Queries are evaluated by locating the grid nodes containing the needed information, and choosing (among these nodes) the most convenient ones, according to a cost model. © Springer-Verlag 2004.
2004
3-540-23664-3
Theoretical Computer Science; Computer Science (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/169948
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