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The 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.

The Worldwide Sensor Web has been applied for monitoring the physical world with spatial and temporal scales that were impossible in the past. With the development of sensor technologies and interoperable open standards, sensor webs generate tremendous volumes of priceless data, enabling scientists to observe previously unobservable phenomena. With its powerful monitoring capability, the sensor web is able to capture time-critical events and provide up-to-date information to support decision-making. In order to harvest the full potential of the sensor web, efficiently processing sensor web data and providing timely notifications are necessary. Therefore, we aim to design a software component applying the publish/subscribe communication model for the sensor web. However, as sensor web data are geospatial in nature, existing topological operators are inefficient when processing a large number of geometries. This paper presents the Aggregated Hierarchical Spatial Model (AHS model) to efficiently determine topological relationships between sensor data and predefined query objects. By using a predefined hierarchical spatial framework to index geometries, the AHS model can match new sensor data with all subscriptions in a single process to improve the query performance. Based on our evaluation results, the query latency of the AHS model increases 2.5 times more slowly than that of PostGIS. As a result, we believe that the AHS model is able to more efficiently process topological operators in a sensor web publish/subscribe system.

With the advance of the World-Wide Web (WWW) technology, people can easily share content on the Web, including geospatial data and web services. Thus, the “big geospatial data management” issues start attracting attention. Among the big geospatial data issues, this research focuses on discovering distributed geospatial resources. As resources are scattered on the WWW, users cannot find resources of their interests efficiently. While the WWW has Web search engines addressing web resource discovery issues, we envision that the geospatial Web (i.e., GeoWeb) also requires GeoWeb search engines. To realize a GeoWeb search engine, one of the first steps is to proactively discover GeoWeb resources on the WWW. Hence, in this study, we propose the GeoWeb Crawler, an extensible Web crawling framework that can find various types of GeoWeb resources, such as Open Geospatial Consortium (OGC) web services, Keyhole Markup Language (KML) and Environmental Systems Research Institute, Inc (ESRI) Shapefiles. In addition, we apply the distributed computing concept to promote the performance of the GeoWeb Crawler. The result shows that for 10 targeted resources types, the GeoWeb Crawler discovered 7351 geospatial services and 194,003 datasets. As a result, the proposed GeoWeb Crawler framework is proven to be extensible and scalable to provide a comprehensive index of GeoWeb.

The 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. [ABSTRACT FROM AUTHOR]