Guan Jihong

GQL: Extending Xquery to query GML documents

GML is becoming the de facto standard for electronic data exchange among the applications of Web and distributed geographic information systems. However, the conventional query languages (e.g. SQL and its extended versions) are not suitable for direct querying and updating of GML documents. Even the effective approaches working well with XML could not guarantee good results when applied to GML documents. Although XQuery is a powerful standard query language for XML, it is not proposed for queryings spatial features, which constitute the most important components in GML documents. We proposes GQL, a query language specification to support spatial queries over GML documents by extending XQuery. The data model, algebra, and formal semantics as well as various spatial functions and operations of GQL are presented in detail.

DCAD： a Dual Clustering Algorithm for Distributed Spatial Databases

Spatial objects have two types of attributes: geometrical attributes and non-geometrical attributes, which belong to two different attribute domains (geometrical and non-geometrical domains). Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering: local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted. Second, local features from each site are sent to a central site where global clustering is obtained based on those features. Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.

Building distributed web GIS: a mobile-agent based approach

The diversity of GISs and the wide-spread availability of WWW have led to an increasing amount of research on integrating a variety of heterogeneous and autonomous GISs in a cooperative environment to construct a new generation of GIS characterizing in open architecture, distributed computation, interoperability, and extensibility. Our on-going research project MADGIS (Mobile Agent based Distributed Geographic Information System) is reported, in which we propose the architecture of MADGIS to meet the requirements of integrating distributed GIS applications under Internet environment. We first describe the architecture of MADGIS, and detailed discussions focusing on the structure of client site, server site and mobile agent in MADGIS. Then we explore key techniques for MADGIS implementation.

Hierarchical classification of Chinese documents based onN-grams

We explore the techniques of utilizingN-gram information to categorize Chinese text documents hierarchically so that the classifier can shake off the burden of large dictionaries and complex segmentation processing, and subsequently be domain and time independent. A hierarchical Chinese text classifier is implemented. Experimental results show that hierarchically classifying Chinese text documents basedN-grams can achieve satisfactory performance and outperforms the other traditional Chinese text classifiers.

Building GIS web services on JXTA network

In recent years, Web services and Peer-to-Peer (or simply P2P) appear as two of the hottest research topics in network computing. On the one hand, by adopting a decentralized, network-based style, P2P technologies can make P2P systems enhance overall reliability and fault-tolerance, increase autonomy, and enable ad-hoc communication and collaboration. On the other hand, Web services provides a good approach to integrate various heterogeneous systems and applications into a cooperative environment. This paper presents the techniques of combining Web services and P2P technologies into GIS to construct a new generation of GIS, which is more flexible and cooperative. As a case study, an ongoing project JGWS is introduced which is an experimental GIS Web services platform built on JXTA. This paper also explores the schemes of building GIS Web services in a P2P environment.

Scaling up the DBSCAN Algorithm for Clustering Large Spatial Databases Based on Sampling Technique

Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and etc. We combine sampling technique with DBSCAN algorithm to cluster large spatial databases, and two sampling-based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN, and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering largescale spatial databases.