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Analytic Methods for Spatio-Temporal Data in a Nature-Inspired Data Model

We are surrounded by information and much of it needs to be stored and analysed. Data analysis would be easier if the data storage structure were closer to that of a natural data structure. Many storage structures and related methods have been proposed in recent years due to the importance of understanding spatio-temporal information associated with a particular place and time. In this paper, some of the most important analytic methods for spatio-temporal data are considered and categorized in terms of their algorithms. We also describe the difficulties of knowledge representation when dealing with spatio-temporal data. In addition, three of the analytic functions of theHair-oriented Data Model are defined, which is a nature-inspired solution. These analytic functions are implemented in Oracle and tested on climate change data as a case study. The main objectives of this research are to propose a model to achieve better knowledge representation, provide the capability to expand queries through additional analytical attributes and reduce redundancy, and thereby obtain better integrity and consistency in spatio-temporal databases