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Fan, MH; Fan, H; Chen, NC; Chen, ZQ; Du, W (2013). Active on-demand service method based on event-driven architecture for geospatial data retrieval. COMPUTERS & GEOSCIENCES, 56, 1-11.

Timely on-demand access to geospatial data is necessary for environmental observation and disaster response. However, traditional service methods for acquiring geospatial data are inefficient and cumbersome, which is not beneficial for timely data acquisition. In these service methods, data are obtained and published by managers and are then left to users to discover and to retrieve them. To solve this problem, we propose an event-driven active on-demand data service method, for which a prototype based on sensor web technologies is demonstrated. First, we select a subset of observed properties as the attributes of an observation event of a data service system. Event-filtering technologies are then employed to find the data desired by users. Finally, the data that meet the subscription requirement are pushed to subscribers on time. The aims of the implementation of the method are to test the suitability of the observation and measurement (O&M) profile for Earth observation and OGC event pattern markup language (EML) specification. We determined the attributes of observation events according to the requirement of the data service and encoded observation event information using the OGC Observations and Measurements specification. We encoded the information under filtering conditions using the OGC Event Pattern Markup Language specification. We implemented a data service method that is based on event-driven architecture via a combination of some sensor web enablement services. Finally, we verified the feasibility of the method using MODIS data from the forest fires that occurred on February 7, 2009, in Victoria, Australia. The results show that the proposed method can achieve actively pushing the desired data to subscribers in the shortest possible time. O&M profiles for Earth observation and EML are suitable for the metadata encoding of observation events and the encoding of subscription information respectively. They match well for the data service in the system. (C) 2013 Elsevier Ltd. All rights reserved.



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