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The major drawback of this method is that still all results have to be materialized, before sorting them into their zones. Therefore, it might not be suitable for production use, even though it proves so flexible. 3 Adaptive Weight Adjustment The third method makes use of special indices, such as proposed by (Papadias et al. 2003) which allow sky-band queries. They allow for a maximum of flexibility but require pre-computation and maintenance of the indices. However, they could prove crucial for a timely execution of a query.
A notional view of the architecture is shown in Figure 1. Figure 1. Overall Agent Architecture. Table 1. Agent Types and Functionality. Software Package Functionality Java Advanced Imaging API Image processing algorithms OpenMap Geospatial visualization and manipulation Google API Web searching via the Google search engine GATE API Natural language processing (NLP) WEKA API Data-mining, clustering, and classification SMART Algorithm Relevancy ranked indexing of documents A quantitative analysis of a mobile 31 We validate the architecture by taking several legacy software applications and representing them as distributed collaborative agents.
Spatial datasets are also indexed by spatial (bounding rectangle) and temporal dimensions. – Hosted: whether the dataset is hosted within the Grid environment, or remains external – Schema: for relational data sources, includes schemas and exported function names (the schema extraction is described in (Gupta et al. 2002)). For XML (GML) sources, it includes XML schema and supported operations. For ArcIMS sources, for example, the registry keeps, beyond schema elements in ArcCatalog-generated layer metadata, records of grid services at each source that implement ArcXML’s GET_SERVICE_INFO, GET_FEATURE_COUNT, GET_IMAGE, GET_FEATURES and GET_EXTRACT requests used by the map assembly services (Zaslavsky et al.