This chapter provides an overview of the current state-of-the-art approach of distributed geoprocessing by describing the related concepts, such as the OGC Web Processing Service, workflows, Quality of Service and legacy system integration. Furthermore, the chapter demonstrates different applications for distributed geoprocessing. Finally, this chapter examines the introduced concepts by two scenarios.
A lot of effort has been invested in Spatial Data Infrastructures (SDIs) during the last decade regarding interoperable standards for services and data. But still the scalability and performance of SDI services is reported to be crucial especially if they are accessed concurrently by a high number of users. Furthermore, laws and provisions such as the INSPIRE directive specify challenging requirements regarding the performance, availability and scalability of SDI services. This article presents a Hybrid Cloud architecture for matching INSPIRE-related Quality of Service (QoS) requirements, without investing in rarely used hardware in advance, by occupying external third-party resources on a pay-as-you-go basis. The presented Hybrid Cloud is a composition of a local IT-infrastructure (Private Cloud) and the computational resources of third-party vendors (Public Cloud). The local infrastructure is laid out to handle the average main load of a service and in lasting peak times additional resources of external providers are allocated and integrated on demand into the local infrastructure to provide sufficient service quality automatically. A proof-of-concept implementation of the proposed Hybrid Cloud approach is evaluated and benchmarked with respect to INSPIRE-related QoS requirements.
Abstract. To realize live geoinformation, which is about providing information as soon as it is available, new approaches for instant geoprocessing and efficient resource utilization are required. Currently, such geoprocessing on the web is handled sequentially instead. This article describes a new approach by processing geodata streams and thereby enabling a continuous processing for improved resource utilization rates. In particular, this work applies HTTP Live Streaming for the example of standardized geoprocessing services. The approach is evaluated for processing large volume datasets of OpenStreetMap data. The presented implementation is based on Free and Open Source software.
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