A Bottom-Up Approach for Automatically Grouping Sensor Data Layers by their Observed Property
AbstractThe Sensor Web is a growing phenomenon where an increasing number of sensors are collecting data in the physical world, to be made available over the Internet. To help realize the Sensor Web, the Open Geospatial Consortium (OGC) has developed open standards to standardize the communication protocols for sharing sensor data. Spatial Data Infrastructures (SDIs) are systems that have been developed to access, process, and visualize geospatial data from heterogeneous sources, and SDIs can be designed specifically for the Sensor Web. However, there are problems with interoperability associated with a lack of standardized naming, even with data collected using the same open standard. The objective of this research is to automatically group similar sensor data layers. We propose a methodology to automatically group similar sensor data layers based on the phenomenon they measure. Our methodology is based on a unique bottom-up approach that uses text processing, approximate string matching, and semantic string matching of data layers. We use WordNet as a lexical database to compute word pair similarities and derive a set-based dissimilarity function using those scores. Two approaches are taken to group data layers: mapping is defined between all the data layers, and clustering is performed to group similar data layers. We evaluate the results of our methodology.
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Knoechel, B.; Huang, C.-Y.; Liang, S.H. A Bottom-Up Approach for Automatically Grouping Sensor Data Layers by their Observed Property. ISPRS Int. J. Geo-Inf. 2013, 2, 1-26.
Knoechel B, Huang C-Y, Liang SH. A Bottom-Up Approach for Automatically Grouping Sensor Data Layers by their Observed Property. ISPRS International Journal of Geo-Information. 2013; 2(1):1-26.Chicago/Turabian Style
Knoechel, Ben; Huang, Chih-Yuan; Liang, Steve H. 2013. "A Bottom-Up Approach for Automatically Grouping Sensor Data Layers by their Observed Property." ISPRS Int. J. Geo-Inf. 2, no. 1: 1-26.