Hair-oriented data model for spatio-temporal data mining

A. Madraky, Zulaiha Ali Othman, Abdul Razak Hamdan

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Spatio-temporal data are complex in terms of number of attributes for spatial and temporal values, and the data are changing towards time. Traditional method to mining the spatio-temporal data is the fact that the data is stored in data warehouse in un-normalization form as union of spatial and temporal data know as tabular data warehouse. A Hair-Oriented Data Model (HODM) has been proved as a suitable data model for spatio-temporal data. It has reduced the file size and decreased query execution time. The spatio-temporal data stored using the HODM known as Hair-Oriented Data warehouse. However, this paper aims to presents a method to develop spatio-temporal data mining model using the Hair-Oriented data warehouse. The Hair- Oriented data model also provide with various functions for easy maintenance on spatio-temporal data warehouse. Experiment conducted using Climate-change spatio-temporal data set benchmark. Two Climate-change spatio-temporal models been developed using regression and knearest neighbor techniques. The performance of the Hair-Oriented Data Warehouse is evaluated by comparing its performance with traditional tabular data warehouse. The result shows that developing data mining spatio-temporal model using Hair-Oriented data warehouse is faster compare using the tabular data warehouse, therefore it can be concluded that the Hair-Oriented Data Model is suitable for Spatio-temporal data mining.

Original languageEnglish
Pages (from-to)90-101
Number of pages12
JournalInternational Review on Computers and Software
Volume10
Issue number1
Publication statusPublished - 2015

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Data warehouses
Data mining
Data structures
Climate change

Keywords

  • Data warehouse models
  • Hair-oriented data model
  • Spatio-temporal data mining

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Hair-oriented data model for spatio-temporal data mining. / Madraky, A.; Ali Othman, Zulaiha; Hamdan, Abdul Razak.

In: International Review on Computers and Software, Vol. 10, No. 1, 2015, p. 90-101.

Research output: Contribution to journalArticle

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