Towards Ontology-based Data Quality Inference in Large-Scale Sensor Networks
Cluster, Cloud and Grid Computing(2012)
Abstract
This paper presents an ontology-based approach for data quality inference on streaming observation data originating from large-scale sensor networks. We evaluate this approach in the context of an existing river basin monitoring program called the Intelligent River®. Our current methods for data quality evaluation are compared with the ontology-based inference methods described in this paper. We present an architecture that incorporates semantic inference into a publish/subscribe messaging middleware, allowing data quality inference to occur on real-time data streams. Our preliminary benchmark results indicate delays of 100ms for basic data quality checks based on an existing semantic web software framework. We demonstrate how these results can be maintained under increasing sensor data traffic rates by allowing inference software agents to work in parallel. These results indicate that data quality inference using the semantic sensor network paradigm is viable solution for data intensive, large-scale sensor networks.
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Key words
large-scale sensor networks,real-time data stream,towards ontology-based data quality,ontology-based inference method,data quality evaluation,large-scale sensor network,semantic inference,sensor data traffic rate,inference software agent,observation data,data quality inference,basic data quality check,river basin,semantic web,distributed computing,data quality,sensors,real time data,owl,ontologies,wireless sensor network,sensor network,sensor fusion,semantics,software agent,publish subscribe,software framework,wireless sensor networks,middleware
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