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Geospatial Web Services are data-oriented services, which include a variety of complex data models and metadata. Discovering the appreciate services with related geospatial datasets among a large number of available ones is a key task in the Geospatial Web Services domain. This paper proposes a peer-to-peer (P2P) based approach for discovering geospatial Web Services. We characterize the geospatial Web Services profile as a set of keywords including the metadata attributes, the minimum bounding rectangle (MBR) and the QoS parameters. Differing from the keywords based P2P Web Services discovery approaches, we use the MBR information to cluster and index the services into a kind of Peer R+ tree. With the tree, a P2P system can support complex queries containing partial keywords and spatial querying. The approach has been used in the Beijing Spatial Data Infrastructure project. We implement an peer-to-peer geospatial Web Services discovery system prototype that shows our approach facilitates complex service queries that contains spatial keywords.

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.

Outlier detecting is one of the most important data analysis technologies in data mining, which can be used to discover anomalous phenomena in huge dataset. Many literatures on spatial outlier detecting and time series outlier detecting have appeared, while the area of spatio-temporal outliers considering both spatial and temporal dimensions has still rarely been touched. Defining outliers in traditional dataset is more explicit because the data structure we need to focus on is very straightforward (e.g., a spatial point or a transaction record). However, it is much more difficult to give outlier a definite characterization in spatio-temporal lattice data, since there are so many data structures we can pay attention to. With the aim of detecting useful and meaningful outliers in climate dataset, we introduce a formalized way to define outliers in spatio-temporal lattice data, in which the importance of clarifying basic data structure (we call it basic element in our paper) is stressed. As a case study, we define two kinds of spatio-temporal outliers based on a global climate dataset, according to the three aspects we propose in defining an outlier. The introduction of basic element and the formulation of outlier definition process make it easier and clearer to define meaningful outliers. Thus outlier detecting in spatio-temporal lattice data will provide us with really interesting and useful knowledge.

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.