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

  • Authors:

  • Longzhuang Li;Yonghuai Liu;Anil Kumar Nalluri;Chunhui Jin

  • Affiliations:

  • Dept. of Computing Sciences, Texas A&M Uni.-Corpus Christi, Corpus Christi, USA TX 78412;Dept. of Computer Science, Uni. Of Wales, Aberystwyth, UK SY 23 DB;Dept. of Computing Sci., Texas A&M Uni.-CC, Corpus Christi, USA;Dept. of Computing Sci., Texas A&M Uni.-CC, Corpus Christi, USA

  • Venue:
  • APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
  • Year:
  • 2009

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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.