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