research-article

Quality metrics for conceptual models for data warehouse focusing on dimension hierarchies

Authors:

Sushama Nagpal,

Sangeeta SabharwalAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 36, Issue 4

Pages 1 - 5

https://doi.org/10.1145/1988997.1989015

Published: 04 August 2011 Publication History

Abstract

Multidimensional conceptual models have been accepted as the foundation for data warehouse designs. The quality of these models have significant effect on the quality of data warehouse and hence, in turn on the information quality. Few researchers have defined quality attributes for the conceptual models for data warehouse and have also proposed metrics to assess the quality attributes of these models objectively. The objective of this work is to propose candidate metrics to compute the structural complexity of multidimensional model. The main emphasis of this paper will be on the dimension hierarchies in multidimensional model. Though, these hierarchies play very significant role in analysing data at various granularity levels, their use enhances structural complexities of multidimensional model which can affect their understandability and modifiability and in turn maintainability.

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Cited By

Gosain ASingh J(2020)Comprehensive complexity metric for data warehouse multidimensional model understandabilityIET Software10.1049/iet-sen.2019.0150Online publication date: 24-Jan-2020
https://doi.org/10.1049/iet-sen.2019.0150
(2019)Empirical investigation of dimension hierarchy sharing-based metrics for multidimensional schema understandabilityInternational Journal of Intelligent Engineering Informatics10.5555/3337636.33376387:2-3(141-163)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.5555/3337636.3337638
Javed MLin Y(2019)Automated Assessment of ER Model Using the Domain KnowledgeComputer and Information Science10.1007/978-3-030-25213-7_10(143-162)Online publication date: 7-Aug-2019
https://doi.org/10.1007/978-3-030-25213-7_10
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Index Terms

Quality metrics for conceptual models for data warehouse focusing on dimension hierarchies
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics
2. Information systems
  1. Data management systems
    1. Database administration
    2. Database design and models

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Published In

cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 36, Issue 4

July 2011

142 pages

ISSN:0163-5948

DOI:10.1145/1988997

Issue’s Table of Contents

Copyright © 2011 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 August 2011

Published in SIGSOFT Volume 36, Issue 4

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Cited By

Gosain ASingh J(2020)Comprehensive complexity metric for data warehouse multidimensional model understandabilityIET Software10.1049/iet-sen.2019.0150Online publication date: 24-Jan-2020
https://doi.org/10.1049/iet-sen.2019.0150
(2019)Empirical investigation of dimension hierarchy sharing-based metrics for multidimensional schema understandabilityInternational Journal of Intelligent Engineering Informatics10.5555/3337636.33376387:2-3(141-163)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.5555/3337636.3337638
Javed MLin Y(2019)Automated Assessment of ER Model Using the Domain KnowledgeComputer and Information Science10.1007/978-3-030-25213-7_10(143-162)Online publication date: 7-Aug-2019
https://doi.org/10.1007/978-3-030-25213-7_10
Edoh-Alove EBimonte SBédard YPinet F(2018)A hybrid risk-aware design method for spatial datacubes handling spatial vague dataInternational Journal of Business Intelligence and Data Mining10.1504/IJBIDM.2014.0683669:3(210-232)Online publication date: 15-Dec-2018
https://dl.acm.org/doi/10.1504/IJBIDM.2014.068366
Gosain ASingh J(2018)Investigating structural metrics for understandability prediction of data warehouse multidimensional schemas using machine learning techniquesInnovations in Systems and Software Engineering10.1007/s11334-017-0308-z14:1(59-80)Online publication date: 14-Dec-2018
https://dl.acm.org/doi/10.1007/s11334-017-0308-z
Gosain ASingh J(2018)A Formal Approach for Evaluating Data Warehouse MetricsSmart Trends in Information Technology and Computer Communications10.1007/978-981-13-1423-0_26(236-243)Online publication date: 21-Aug-2018
https://doi.org/10.1007/978-981-13-1423-0_26
Subramanian GWang K(2017)Systems Dynamics-Based Modeling of Data Warehouse QualityJournal of Computer Information Systems10.1080/08874417.2017.1383863(1-8)Online publication date: 24-Oct-2017
https://doi.org/10.1080/08874417.2017.1383863
Gosain ASingh J(2017)Quality metrics emphasizing dimension hierarchy sharing in multidimensional models for data warehouse: a theoretical and empirical evaluationInternational Journal of System Assurance Engineering and Management10.1007/s13198-017-0641-58:S2(1672-1688)Online publication date: 17-Jun-2017
https://doi.org/10.1007/s13198-017-0641-5
Sabharwal SNagpal SAggarwal G(2015)Empirical investigation of metrics for multidimensional model of Data Warehouse using Support Vector Machine2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)10.1109/ICRITO.2015.7359260(1-5)Online publication date: Sep-2015
https://doi.org/10.1109/ICRITO.2015.7359260
Gosain AHeena (2015)Literature Review of Data Model Quality Metrics of Data WarehouseProcedia Computer Science10.1016/j.procs.2015.04.17648(236-243)Online publication date: 2015
https://doi.org/10.1016/j.procs.2015.04.176
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