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

  • Longzhuang Li
  • Yonghuai Liu
  • Anil Kumar Nalluri
  • Chunhui Jin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5446)

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.

Keywords

Data integration qualitative spatial representation and reasoning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Longzhuang Li
    • 1
  • Yonghuai Liu
    • 2
  • Anil Kumar Nalluri
    • 3
  • Chunhui Jin
    • 3
  1. 1.Dept. of Computing SciencesTexas A&M Uni.-Corpus ChristiCorpus ChristiUSA
  2. 2.Dept. of Computer ScienceUni. Of WalesAberystwythUK
  3. 3.Dept. of Computing Sci.Texas A&M Uni.-CCCorpus ChristiUSA

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