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GIS is a good spatial analysis tool, nevertheless, as the accumulation of spatial data, the functions that GIS offers are not enough. Spatial data mining, which can automatically discover implicit knowledge from spatial data, has recently received wide attention. However, the spatial data mining systems are not competent for preprocessing and presenting spatial data. Hence, the idea to integrate spatial data mining and GIS is straightforward. In this paper, we propose a novel method to integrate spatial data mining and GIS. This method is implemented with SPMML, an XML-based language that is extended from PMML. We build a prototype system with this method and it works well.

Spatial data mining is a promising technique that deals with extraction of implicit knowledge or other interesting patterns from large amount of spatial data. Though most data mining systems work with data stored in flat files or operational database, it has been recognized that mining in a data warehouse usually result in better information. Because data are usually cleansed before they are stored into data warehouse. Furthermore, data warehouse provides data with different levels of summarization for the clients, which will lead to fruitful data mining. However, current techniques of data warehouse can not handle spatial data well. Both dimensions and measures in the data model of data warehouse are nonspatial data. In this paper, we propose a new data model called spatial data cube for data warehouse. Spatial data cube supports both spatial and nonspatial data. We also introduce how to construct a spatial data cube that can answer queries efficiently by selective materialization. We believe that the spatial data cube can provide better support for spatial data mining.

The location technologies, such as GPS and telegraphy, are producing more and more data of moving objects. Spatio-temporal database is needed to manage these data, so as to solve the problems in spatio-temporal applications. However, there are few prototypes of spatio-temporal database systems yet. This is because attention is always paid on some specified aspects not the whole system. In this paper, we present the design and implementation of a spatio-temporal database prototype for managing moving objects. A Trajectory Model is proposed in this paper, where the moving objects' movements in 2D plane over time are treated as trajectories in the 3D spatio-temporal space. Referring to R tree and the trajectory oriented indexing method TB tree, a new indexing method: S-TB tree is proposed in the prototype. Distance based queries and trajectory topology based queries are all supported by the prototype. The distance based queries includes point queries, range queries. This paper also presents different strategies to process these queries. Finally, two application cases are presented: traffic control and historical queries of the storms on Earth.

GIS Web service turns out to be one of the new directions of the paradigm of GIS, due to the popular use of the Internet and the dramatic progress of telecommunications technology. However, focused on the description of the interface of functions by Web service description language (WSDL), the general Web service technologies are not sufficient to model GIS Web service with data-oriented characteristic, especially those data provider services and data transform services. In this paper, we propose geographic-Web service description language (abbr. G-WSDL) which enhances the ability of describing such data-driven services by extending the current WSDL with service metadata. Two application scenarios will be presented at last to show its strength in automatic GIS Web service discovery and dynamic services combination.

Spatial sampling is a significant part of query processing in spatial database. In this paper, an adaptive data-driven sampling for selectivity estimation is proposed in spatial database. This technique presents an efficient sampling for spatial data, especially for two-dimensional line and polygon data, and it make the sample size fit the limit of time and memory, or a user-defined parameter. The data-driven sampling technique is compared with various techniques on different type of datasets in our experimental study, and it out outperforms the other techniques over a broad range of query workloads and datasets.