Abstract
The Sensor Web provides an interoperable way to discover, task, and retrieve sensor data over the network. The approach to knowledge discovery from the Sensor Web needs to explore data ingestion, domain models, and data processing. This chapter leverages recent advances in autonomous control and the Semantic Web technologies to present a systematic approach in which the Sensor Web and Earth science models are annotated with semantic metadata and chained together in an autonomous way as a service chain to simulate the process of Sensor Web mining based on semantic inference. This approach significantly advances sensor data exploration with semantics in an interoperable way to improve the accuracy and timeliness of monitoring and predicting rapidly changing Earth phenomena.
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- 1.
Earth Science Gateway, http://esg.gsfc.nasa.gov
- 2.
GeoBrain GRASS Web Services, http://geobrain.laits.gmu.edu:81/grassweb/manuals
- 3.
ADaM Web Services, http://datamining.itsc.uah.edu/adam/
- 4.
Dublin Core Metadata, http://dublincore.org
- 5.
OpenCyc, http://www.opencyc.org
- 6.
OWL-based Web Service Ontology, http://www.daml.org/services/owl-s/1.1
- 7.
SWRL: A Semantic Web Rule Language Combining OWL and RuleML, http://www.w3.org/Submission/SWRL
- 8.
Web Services Business Process Execution Language Version 2.0, http://docs.oasis-open.org/wsbpel/2.0/OS/wsbpel-v2.0-OS.html
- 9.
Web Services Addressing (WS-Addressing), http://www.w3.org/Submission/ws-addressing/
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Zhao, P., Di, L., Yu, G. (2010). Toward Autonomous Mining of the Sensor Web. In: Chen, H., Wang, Y., Cheung, KH. (eds) Semantic e-Science. Annals of Information Systems, vol 11. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-5908-9_8
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