A Metamodeling Approach to Creating Data Models for Geospatial Datasets with Persistent Correlations

A. Sekar and A.H. Lee (USA)


Data modeling, geospatial data, and metamodeling


In the last decade, the amount of geospatial data collected has been phenomenal. Each day terabytes of data are collected by government and commercial data providers using everything from inexpensive handheld GPS devices to complex satellite systems. Using these data, however, is proving to be costly and difficult. Because of the diversity in the types of geospatial data and the different ways they are used, creating standards for the organization of this data has seemed intractable. In this paper, we take a more global approach to this problem. By examining the fundamental structure of all geospatial data and by breaking down all the processes of using these data to a few basic operations, we create a framework for data models which can be used to represent any geospatial data and allows for the efficient use (and reuse) of these data. A metamodeling approach using two meta-layers is used to define this framework. This approach facilitates the creation of systems that are data model agile.

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