疾病关系分析的模糊区域连接演算的空间数据库实现
Spatial database implementation of fuzzy region connection calculus for analysing the relationship of diseases
摘要：
分析海量的空间数据在医疗、城市规划、农业等新兴分析和决策领域起着重要作用，为了从地理数据中提取有意义的知识，需要分析空间数据对象之间的关系。一类重要的关系是拓扑关系，如区域之间的连通性或重叠。而现实世界中的地理区域，如湖泊或森林，没有确切的边界，是模糊的，大多数现有的分析方法忽略了拓扑关系的这一固有特性。提出了一种基于模糊区域连接演算（RCC）的空间数据库拓扑关系处理方法。该方法在PostGIS空间数据库中实现，并在分析疾病之间的关系作为一个重要的应用领域进行了评估。我们还使用我们的模糊RCC实现模糊化的天际线运营商在空间数据库。评估的结果表明，我们的方法提供了一个更现实的空间关系视图，并提供了更大的灵活性，数据分析师提取有意义的和准确的结果相比，现有的方法。
英文摘要：
Analyzing huge amounts of spatial data plays an important role in many emerging analysis and decision-making domains such as healthcare, urban planning, agriculture and so on. For extracting meaningful knowledge from geographical data, the relationships between spatial data objects need to be analyzed. An important class of such relationships are topological relations like the connectedness or overlap between regions. While real-world geographical regions such as lakes or forests do not have exact boundaries and are fuzzy, most of the existing analysis methods neglect this inherent feature of topological relations. In this paper, we propose a method for handling the topological relations in spatial databases based on fuzzy region connection calculus (RCC). The proposed method is implemented in PostGIS spatial database and evaluated in analyzing the relationship of diseases as an important application domain. We also used our fuzzy RCC implementation for fuzzification of the skyline operator in spatial databases. The results of the evaluation show that our method provides a more realistic view of spatial relationships and gives more flexibility to the data analyst to extract meaningful and accurate results in comparison with the existing methods.
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