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Qualitative Spatial Representation and Reasoning for Data Integration of Ocean Observational Systems

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Advances in Data and Web Management (APWeb 2009, WAIM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5446))

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Abstract

Spatial features are important properties with respect to data integration in many areas such as ocean observational information and environmental decision making. In order to address the needs of these applications, we have to represent and reason about the spatial relevance of various data sources. In the paper, we develop a qualitative spatial representation and reasoning framework to facilitate data retrieval and integration of spatial-related data from ocean observational systems, such as in situ observational stations in the Gulf of Mexico. In addition to adopt the state-of-the-art techniques to represent partonomic, distance, and topological relations, we develop a probability-based heuristic method to uniquely infer directional relations between indirectly connected points. The experimental results show that the proposed method can achieve the overall adjusted correct ratio of 87.7% by combining qualitative distance and directional relations.

Research is supported in part by the National Science Foundation under grant CNS-0708596 and CNS-0708573.

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Author information

Authors and Affiliations

  1. Dept. of Computing Sciences, Texas A&M Uni.-Corpus Christi, Corpus Christi, TX 78412, USA

    Longzhuang Li

  2. Dept. of Computer Science, Uni. Of Wales, Aberystwyth, Ceredigion, SY 23 DB, UK

    Yonghuai Liu

  3. Dept. of Computing Sci., Texas A&M Uni.-CC, Corpus Christi, TX, USA

    Anil Kumar Nalluri & Chunhui Jin

Authors
  1. Longzhuang Li
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  2. Yonghuai Liu
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  3. Anil Kumar Nalluri
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  4. Chunhui Jin
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Editor information

Editors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, China

    Qing Li

  2. Department of Computer Science & Technology, Tsinghua University, Beijing, China

    Ling Feng

  3. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby BC, Canada

    Jian Pei

  4. Department of Computer Science, University of Vermont, VT 05405, Burlington, USA

    Sean X. Wang

  5. School of Information Technology and Electrical Engineering, The University of Queensland, QLD 4072, Brisbane, Australia

    Xiaofang Zhou

  6. Jiangsu Provincial Key Lab of Computer Information Processing Technology School of Computer Science & Technology, Soochow University China, 1 shizi Street Suzhou, 215006, Jiangsu, China

    Qiao-Ming Zhu

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© 2009 Springer-Verlag Berlin Heidelberg

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Li, L., Liu, Y., Nalluri, A.K., Jin, C. (2009). Qualitative Spatial Representation and Reasoning for Data Integration of Ocean Observational Systems. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, QM. (eds) Advances in Data and Web Management. APWeb WAIM 2009 2009. Lecture Notes in Computer Science, vol 5446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00672-2_37

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  • DOI: https://doi.org/10.1007/978-3-642-00672-2_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00671-5

  • Online ISBN: 978-3-642-00672-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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