Spatial Data

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Nebojša Stefanović - One of the best experts on this subject based on the ideXlab platform.

  • GeoMiner: a system prototype for Spatial Data mining
    Proceedings ACM SIGMOD International Conference on Management of Data, SIDMOD '97, 1997
    Co-Authors: Jia Wei Han, Krzysztof Koperski, Nebojša Stefanović
    Abstract:

    Spatial Data mining is to mine high-level Spatial information and knowledge from large Spatial Databases. A Spatial Data mining system prototype, GeoMiner, has been designed and developed based on our years of experience in the research and development of relational Data mining system, DBMiner, and our research into Spatial Data mining. The Data mining power of GeoMiner includes mining three kinds of rules: characteristic rules, comparison rules, and association rules, in geo-Spatial Databases, with a planned extension to include mining classification rules and clustering rules. The SAND (Spatial And NonSpatial Data) architecture is applied in the modeling of Spatial Databases, whereas GeoMiner includes the Spatial Data cube construction module, Spatial on-line analytical processing (OLAP) module, and Spatial Data mining modules. A Spatial Data mining language, GMQL (Geo-Mining Query Language), is designed and implemented as an extension to Spatial SQL [3], for Spatial Data mining. Moreover, an interactive, user-friendly Data mining interface is constructed and tools are implemented for visualization of discovered Spatial knowledge.

Jia Wei Han - One of the best experts on this subject based on the ideXlab platform.

  • GeoMiner: a system prototype for Spatial Data mining
    Proceedings ACM SIGMOD International Conference on Management of Data, SIDMOD '97, 1997
    Co-Authors: Jia Wei Han, Krzysztof Koperski, Nebojša Stefanović
    Abstract:

    Spatial Data mining is to mine high-level Spatial information and knowledge from large Spatial Databases. A Spatial Data mining system prototype, GeoMiner, has been designed and developed based on our years of experience in the research and development of relational Data mining system, DBMiner, and our research into Spatial Data mining. The Data mining power of GeoMiner includes mining three kinds of rules: characteristic rules, comparison rules, and association rules, in geo-Spatial Databases, with a planned extension to include mining classification rules and clustering rules. The SAND (Spatial And NonSpatial Data) architecture is applied in the modeling of Spatial Databases, whereas GeoMiner includes the Spatial Data cube construction module, Spatial on-line analytical processing (OLAP) module, and Spatial Data mining modules. A Spatial Data mining language, GMQL (Geo-Mining Query Language), is designed and implemented as an extension to Spatial SQL [3], for Spatial Data mining. Moreover, an interactive, user-friendly Data mining interface is constructed and tools are implemented for visualization of discovered Spatial knowledge.

  • Spatial Data Mining: Progress and Challenges Survey Paper
    SIGMOD Workshop on Research Issues on data Mining and Knowledge Discovery (DMKD}, 1996
    Co-Authors: Krzysztof Koperski, Junas Adhikary, Jia Wei Han
    Abstract:

    Spatial Data mining, i.e., mining knowledge from large amounts of Spatial Data, is a highly demanding field because huge amounts of Spatial Data have been collected in various applications, ranging from remote sensing, to geographical information systems (GIS), computer cartography, environmental assessment and planning, etc. The collected Data far exceeded human's ability to analyze. Recent studies on Data mining have extended the scope of Data mining from relational and transactional Databases to Spatial Databases. This paper summarizes recent works on Spatial Data mining, from Spatial Data generalization, to Spatial Data clustering, mining Spatial association rules, etc. It shows that Spatial Data mining is a promising field, with fruitful research results and many challenging issues. 1 Introduction Advances in Database technologies and Data collection techniques including barcode reading, remote sensing, satellite telemetry, etc., have collected huge amounts of Data in large Data

Wang Shuliang - One of the best experts on this subject based on the ideXlab platform.

  • View-angles of Spatial Data mining
    Journal of Tsinghua University, 2020
    Co-Authors: Wang Shuliang
    Abstract:

    This paper proposes a view-angle of Spatial Data mining to match the various demands on Spatial Data mining.Describing the various demands from different users under different Spatial conditions,the view-angles of Spatial Data mining is to discover the knowledge with various granularities from amounts of Spatial Data by using a certain mining algorithms.First,the view-angle of Spatial Data mining is defined within its intension and extension.Second,the view-angle based algorithms are presented.Finally,as a case study, the techniques are applied to mine Baota landslide-monitoring Database.The results are satisfactory.

  • The State of the Art of Spatial Data Mining
    Geomatics World, 2020
    Co-Authors: Wang Shuliang
    Abstract:

    Spatial Data mining is one of the techniques to solve the bottleneck on using vast Spatial Data. In this paper,the state of the art of Spatial Data mining is briefed. First,the motivation is analyzed for Spatial Data mining. Second,Spatial Data mining is studied further on its nature and characteristics. Third,the fruitful results in Spatial Data mining are summarized under the umbrella of theories and techniques,the trends of which are also prospected. Finally,the paper is concluded.

Shangping Wang - One of the best experts on this subject based on the ideXlab platform.

  • Spatial Data mining under Smart Earth
    Proceedings - 2011 IEEE International Conference on Granular Computing, GrC 2011, 2011
    Co-Authors: Shangping Wang
    Abstract:

    Complex Spatial Data is the fundamental content of Smart Earth. Spatial Data mining plays an important role in Smart Earth. In this paper, it is proposed that "Smart earth equals to Digital Earth plus the Internet of Things", which brings a new challenge for Spatial Data mining. First, the background of Spatial Data is summarized. Second, Smart Earth is put forward to be an integration of Digital Earth and the Internet of Things. Third, by analyzing the Spatial Data sources, types and structures, it uncovers that Spatial Data is the resource of Smart Earth. Fourth, Spatial knowledge to discover in Smart Earth are analyzed on its connotations, extensions and categories. It indicates that Spatial knowledge is the intelligent sources of Smart Earth. Fifth, based on Spatial Data characteristics and possible methodologies, the framework of Spatial Data mining is given under Smart Earth. Finally, the perspective of applicable fields is prospected for Spatial Data mining under Smart Earth. It is concluded that Spatial Data mining is one of the fundamental tools to achieve Smart Earth.

  • View-angle of Spatial Data mining
    ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS, 2006
    Co-Authors: Shangping Wang, Haning Yuan
    Abstract:

    In order to discover the knowledge with various granularities from\namounts of Spatial Data, a view-angle of Spatial Data mining is\nproposed. First, the view-angle of Spatial Data mining is defined. In\nits context, the essentials of Spatial Data mining are further\ndeveloped. And the view-angle based algorithms are also presented.\nSecond, the view-angles of Baota landslide-monitoring Data mining, and\ntheir pan-hierarchical relationships, are given. Finally, view-angle III\nis taken as a case study to discover quantitative, qualitative and\nvisualized knowledge from Baota landslide-monitoring Databases. The\nresults indicate that the view-angle based Data mining is practical, and\nthe discovered knowledge with various granularities may satisfy Spatial\ndecision-making at different hierarchies.

  • A try for handling uncertainties in Spatial Data mining
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2004
    Co-Authors: Shangping Wang, Deyi Li, Deren Li, Guoqing Chen, Manning Yuan
    Abstract:

    Uncertainties pervade Spatial Data mining. This paper proposes a method of Spatial Data mining handling randomness and fuzziness simultaneously. First, the uncertainties in Spatial Data mining are presented via characteristics, Spatial Data, knowledge discovery and knowledge representation. Second, the aspects of the uncertainties in Spatial Data mining are briefed. They often appear simultaneously, but most of the existing methods cannot deal with Spatial Data mining with more than one uncertainty. Third, cloud model is presented to mine Spatial Data with both randomness and fuzziness. It may also act as an uncertainty transition between a qualitative concept and its quantitative Data, which is the basis of Spatial Data mining in the contexts of uncertainties. Finally, a case study on landslide-monitoring Data mining is given. The results show that the proposed method can well deal with randomness and fuzziness during the process of Spatial Data mining. ©Springer-Verlag 2004.

Deren Li - One of the best experts on this subject based on the ideXlab platform.

  • A perspective of Spatial Data mining
    MIPPR 2005: Geospatial Information Data Mining and Applications, 2005
    Co-Authors: Shuliang Wang, Deren Li
    Abstract:

    This paper presents a perspective of Spatial Data mining. First, the motivation and development of Spatial Data mining are overviewed. Second, the intension and extension of Spatial Data mining concept are presented in the knowledge to be discovered, and the relationships between Spatial Data mining and other subjects. Third, the discovery mechanism is taken as a process of discovering a form of rules plus exceptions at hierarchal view-angles with various thresholds. Fourth, mining granularity is proposed as the measurement of Data, information and knowledge. Fifth, the existing theories and techniques are briefed. Finally, the whole paper is concluded.

  • Distributed Spatial Data Management
    MIPPR 2005: Geospatial Information Data Mining and Applications, 2005
    Co-Authors: Deren Li
    Abstract:

    The complexity of Spatial Data management will be high as the volume of Spatial Data increases rapidly. As the emerging technology, it is an innovative method that grid computing technology is applied to manage geographically distributed, autonomous and heterogeneous Spatial Data. Integrating grid computing with Spatial Data management technology, the authors designed a basic but high extensible framework for managing distributed and large-scale Spatial Data. In this paper we put emphasis upon Spatial metaData service design and Spatial Data grid design.

  • A try for handling uncertainties in Spatial Data mining
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2004
    Co-Authors: Shangping Wang, Deyi Li, Deren Li, Guoqing Chen, Manning Yuan
    Abstract:

    Uncertainties pervade Spatial Data mining. This paper proposes a method of Spatial Data mining handling randomness and fuzziness simultaneously. First, the uncertainties in Spatial Data mining are presented via characteristics, Spatial Data, knowledge discovery and knowledge representation. Second, the aspects of the uncertainties in Spatial Data mining are briefed. They often appear simultaneously, but most of the existing methods cannot deal with Spatial Data mining with more than one uncertainty. Third, cloud model is presented to mine Spatial Data with both randomness and fuzziness. It may also act as an uncertainty transition between a qualitative concept and its quantitative Data, which is the basis of Spatial Data mining in the contexts of uncertainties. Finally, a case study on landslide-monitoring Data mining is given. The results show that the proposed method can well deal with randomness and fuzziness during the process of Spatial Data mining. ©Springer-Verlag 2004.