NDLI: Towards Ontology-based Data Quality Inference in Large-Scale Sensor Networks

Content Provider	ACM Digital Library
Author	Hallstrom, Jason Goasguen, Sebastien Eidson, Gene Esswein, Sam Post, Chris White, David
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.
Starting Page	898
Ending Page	903
Page Count	6
File Format	PDF
ISBN	9780769546919
DOI	10.1109/CCGrid.2012.143
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-05-13
Access Restriction	Subscribed
Subject Keyword	Wireless sensor networks semantic web distributed computing
Content Type	Text
Resource Type	Article

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Towards Ontology-based Data Quality Inference in Large-Scale Sensor Networks