Research On Key Technologies Of Spatial Information Services Aggregation

Spatial Information Services have become the most important way to acquire geospatial information on the open Internet environment. But the variety and uncertainty of the service resources, and the dynamic and complex user requirement blocked people from acquiring the information they needed efficiently and intelligently. The aggregation of Spatial Information Services is a new application schema for acquiring spatial information. Its goal is to composite several services by combining application logic and semantic constrains automatically to satisfy user requirement. In this complex and creative procedure, not only powerful computing environment should be guaranteed, but also the consistent semantic descriptions and understanding, reasonable knowledge representation and rigorous logic deduction should be supported.This thesis attempts to aggregate Spatial Information Services from computing and intelligent perspective. It introduces the theories in Artificial Intelligence and the techniques in Grid Computing to construct intelligent and efficient aggregation of Spatial Information Services. It focuses on the framework of service aggregation, high performance Spatial Information Services, semantic service description registry and discovery. It also studies the key issues on modeling of rule-driven geospatial service chaining. Its main achievement and innovation on models, methods and applications are described as follows.1.Using the subsequent disaster of earthquake scenario, the new requirements on aggregation of Spatial Information Services are analyzed. The semantic ambiguity and low efficiency are the main problems services aggregation is facing. In order to provide high performance distributed parallel computing power the Spatial Information Grid is suitable for environment. Geospatial ontology and description logic as the main knowledge representation frame are the theory base of service aggregation, which provide the consistent semantic representation and the ability of logic deduction. Then the definition and aggregation frame are introduced, the key technologies are discussed.2.The construction and performance improving methods are studied. Based on WSRF specification the current Spatial Information Services are wrapped into stateful resources. In order to provide high performance services, the Grid based geospatial data storage, process and transport are studied. The clustered Hilbert R-Tree spatial index and average divided data method are utilized for building parallel spatial data index. To improve the HTTP bottleneck on data transport, the GridFTP is introduced for spatial data transport and access. The spatial data process services are studied and the interface is extended for grid job submission. Based on Grid resource monitor mechanism and Mobile-Agent, the migratory geospatial information services are put forward.3.The geospatial ontology and semantic descriptions of Spatial Information Services are studied. Based on the granularity and relationship between geospatial knowledge concepts the top level, domain level and application level of geospatial ontology are discussed. Based on the current ISO and OGC standards, the geospatial domain ontology is constructed. The method of mapping elements from UML to OWL is put forward. The semantics of geospatial services are analyzed from four aspects which are data, function, execution and QoS. Based on the geospatial ontology and OWL-S, the geospatial services are formally described.4.Semantic supported service registry and Description Logic supported service matching are studied. First for the lack of semantic supporting, the ebRIM model is extended for registry which mapped the OWL and OWL-S into registry model. The Description Logic and Horn Logic are utilized for description and deduction of geospatial service precondition and postcondition in order to cover the weakness of execution semantic description. Based on the rules and deduction, improved the service execution match making. Above these methods, a multi-step geospatial service match making strategy is put forward.5.The model construction of rule based Spatial Information Services chain and execution are studied. In order to put service aggregation into realization, the four steps of aggregation are brought forward. First the backward search method in Artificial Intelligence is introduced for services planning. The domain rules and instructing rules are studied. The planning result is mapped to service DAG. Then the locally optimal method is introduced for picking up a service chain. Based on the relationship between OWL-S and WSDL, the service instances are binding to service chain. At last two service execution engines are discussed for service chain execution.6.Based on above research, the geospatial information service aggregation platform and sub modules are designed and implemented. With the application of Block lake risk monitor and warning, a use case has been discussed to validate the feasibility and validity of the models and methods.