Integrating Multi-Source Heterogeneous Fuzzy Spatiotemporal Data

The fuzzy spatiotemporal data has obtained increasing attention with the high-speed development of seismic meteorological, and environmental data management. A lot of significant results has been achieved in integrating multi-source fuzzy heterogeneous data. However, there are some aspects could be improved. There are some inaccurate or redundant results due to semantic heterogeneity between different data sources. This paper defines a new fuzzy spatiotemporal semantic model and constructs an RDF global semantic model to solve it. This semantic model elaborates on the relationship between temporal, spatial, and fuzzy attributes of meteorological data. The accuracy of user queries has been improved through this model, which solves the problem of incomplete results caused by unclear query intentions. The existing integration process mainly converts relational data directly into RDF Schema, which cannot solve the problem of structural heterogeneity between different data sources. Our method utilizes the semi structured nature of XML Schema to solve this problem. Firstly, convert the relational data source into an XML Schema, then map the data from these two data sources to the RDF local semantic model, and then convert it into an RDF schema. Therefore, when the underlying structure is from different data sources, the global query view can be performed.