An efficient approach for modeling temporal database with interval-based timestamping in conventional database management systems

Attaching Date and Time to varying data plays a definite role in representing a dynamic domain and resources on the database systems. The conventional database stores current data and can only represent the knowledge in static sense. Time-varying data representations in relational framework are well...

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Main Authors: Alromema, N., Mohd. Rahim, M. S., Albidewi, I.
Format: Article
Published: American Scientific Publishers 2017
Subjects:
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author Alromema, N.
Mohd. Rahim, M. S.
Albidewi, I.
author_facet Alromema, N.
Mohd. Rahim, M. S.
Albidewi, I.
author_sort Alromema, N.
collection ePrints
description Attaching Date and Time to varying data plays a definite role in representing a dynamic domain and resources on the database systems. The conventional database stores current data and can only represent the knowledge in static sense. Time-varying data representations in relational framework are well described in literature. In this paper we propose an approach for modeling First Normal Form (1NF) and interval-based temporal database over conventional Database Management Systems (DBMS). We choose 1NF approach with interval-based temporal data model for the easily implementation in relational framework as well as to provide the proposed temporal data representation with the modeling and querying power of 1NF relational data model.
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institution Universiti Teknologi Malaysia - ePrints
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spelling utm.eprints-808252019-07-23T09:01:56Z http://eprints.utm.my/80825/ An efficient approach for modeling temporal database with interval-based timestamping in conventional database management systems Alromema, N. Mohd. Rahim, M. S. Albidewi, I. QA75 Electronic computers. Computer science Attaching Date and Time to varying data plays a definite role in representing a dynamic domain and resources on the database systems. The conventional database stores current data and can only represent the knowledge in static sense. Time-varying data representations in relational framework are well described in literature. In this paper we propose an approach for modeling First Normal Form (1NF) and interval-based temporal database over conventional Database Management Systems (DBMS). We choose 1NF approach with interval-based temporal data model for the easily implementation in relational framework as well as to provide the proposed temporal data representation with the modeling and querying power of 1NF relational data model. American Scientific Publishers 2017 Article PeerReviewed Alromema, N. and Mohd. Rahim, M. S. and Albidewi, I. (2017) An efficient approach for modeling temporal database with interval-based timestamping in conventional database management systems. Journal of Computational and Theoretical Nanoscience, 14 (9). pp. 4569-4575. ISSN 1546-1955 http://dx.doi.org/10.1166/jctn.2017.6855 DOI:10.1166/jctn.2017.6855
spellingShingle QA75 Electronic computers. Computer science
Alromema, N.
Mohd. Rahim, M. S.
Albidewi, I.
An efficient approach for modeling temporal database with interval-based timestamping in conventional database management systems
title An efficient approach for modeling temporal database with interval-based timestamping in conventional database management systems
title_full An efficient approach for modeling temporal database with interval-based timestamping in conventional database management systems
title_fullStr An efficient approach for modeling temporal database with interval-based timestamping in conventional database management systems
title_full_unstemmed An efficient approach for modeling temporal database with interval-based timestamping in conventional database management systems
title_short An efficient approach for modeling temporal database with interval-based timestamping in conventional database management systems
title_sort efficient approach for modeling temporal database with interval based timestamping in conventional database management systems
topic QA75 Electronic computers. Computer science
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