Geospatial Service Composition Research Articles

Model-driven geospatial web service composition

Abstract. A lot of work has been done on the geospatial service composition to support advanced processing, spatial calculation, and invoking of heterogeneous data. However, the quality of service chain is rarely considered and the process model cannot be reused. A modeldriven way of geospatial web service composition is proposed in this work, the service composition is treated as an optimization problem by GwcsFlow model and dynamic binding mechanism. The case of facility location analysis is provided to demonstrate the improvements in geospatial service composition through optimization algorithms.

Analytical Geospatial Web Services

Analysis other than simple access and visualization is the emerging trend within the web community [Berners-Lee 2007]. With the rapid development of the Internet and web technologies, “the traditional approach to publishing maps on the Internet ‘by GIS experts, for GIS experts’ is outdated, ineffective and unusable as Web mapping becomes more and more mainstream” [van der Vlugt and Stanley 2005]. Over the last few years, we have seen more and more traditional GIS functions being integrated into web mapping/GIS applications in a way transparent to users, using distributed analytical and geoprocessing services coordinated by well-defined workflows [Yang and Raskin 2009]. Web service technology has played one of the key roles in this transition. As the service-driven paradigm continues to dominate web mapping and GIS development, web-based geospatial analytical services have drawn the attention of many in the field, hoping to build such services on top of more mature basic data services. Given the complex nature of analytical services, including modelling, simulation, function and visualization services, the orchestration of web mapping and geoprocessing services from different sources becomes increasingly important. The trend towards analytical geospatial web services continues with intelligent analytical capability, which involves indepth study on dynamic geospatial service composition, for example. It is also expected that intelligent analytical services will be enhanced with further developments in Geographic Knowledge Discovery (GKD), spatial data mining techniques and tools, web-based modelling and simulation, augmented reality applications, agent-based systems and paradigms [Hutchinson and Veenendaal 2011], etc. The articles included in this special issue contribute to the capacity building towards analytical geospatial web. The three feature articles selected for this special issue touch on web geoprocessing, visualization and thematic mapping tools. The issue begins with the article authored by Eberle and Strobl, Web-based