Analytic methods for spatio-temporal data in a nature-inspired data model

Abbas Madraky, Zulaiha Ali Othman, Abdul Razak Hamdan

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

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 spatiotemporal databases.

Original languageEnglish
Pages (from-to)547-556
Number of pages10
JournalInternational Review on Computers and Software
Volume9
Issue number3
Publication statusPublished - 2014

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Knowledge representation
Data structures
Climate change
Redundancy
Data storage equipment

Keywords

  • Analytic methods
  • Data engineering
  • Data mining
  • Hair-oriented data model
  • Spatio-temporal data models

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Analytic methods for spatio-temporal data in a nature-inspired data model. / Madraky, Abbas; Ali Othman, Zulaiha; Hamdan, Abdul Razak.

In: International Review on Computers and Software, Vol. 9, No. 3, 2014, p. 547-556.

Research output: Contribution to journalArticle

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