article

Metrics for data warehouse conceptual models understandability

Authors:

Manuel Serrano,

Mario PiattiniAuthors Info & Claims

Information and Software Technology, Volume 49, Issue 8

Pages 851 - 870

https://doi.org/10.1016/j.infsof.2006.09.008

Published: 01 August 2007 Publication History

Abstract

Due to the principal role of Data warehouses (DW) in making strategy decisions, data warehouse quality is crucial for organizations. Therefore, we should use methods, models, techniques and tools to help us in designing and maintaining high quality DWs. In the last years, there have been several approaches to design DWs from the conceptual, logical and physical perspectives. However, from our point of view, none of them provides a set of empirically validated metrics (objective indicators) to help the designer in accomplishing an outstanding model that guarantees the quality of the DW. In this paper, we firstly summarise the set of metrics we have defined to measure the understandability (a quality subcharacteristic) of conceptual models for DWs, and present their theoretical validation to assure their correct definition. Then, we focus on deeply describing the empirical validation process we have carried out through a family of experiments performed by students, professionals and experts in DWs. This family of experiments is a very important aspect in the process of validating metrics as it is widely accepted that only after performing a family of experiments, it is possible to build up the cumulative knowledge to extract useful measurement conclusions to be applied in practice. Our whole empirical process showed us that several of the proposed metrics seems to be practical indicators of the understandability of conceptual models for DWs.

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Metrics for data warehouse conceptual models understandability

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cover image Information and Software Technology

Information and Software Technology Volume 49, Issue 8

August, 2007

130 pages

ISSN:0950-5849

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Copyright © Elsevier B.V. © 2006.

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Butterworth-Heinemann

United States

Publication History

Published: 01 August 2007

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Antonelli LBimonte SRizzi S(2022)Multidimensional modeling driven from a domain languageAutomated Software Engineering10.1007/s10515-022-00375-530:1Online publication date: 26-Dec-2022
https://dl.acm.org/doi/10.1007/s10515-022-00375-5
Bimonte SAntonelli LRizzi S(2021)Requirements-driven data warehouse design based on enhanced pivot tablesRequirements Engineering10.1007/s00766-020-00331-326:1(43-65)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1007/s00766-020-00331-3
Sautot LBimonte SJournaux L(2020)A Semi-Automatic Design Methodology for (Big) Data Warehouse Transforming Facts into DimensionsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.292562133:1(28-42)Online publication date: 7-Dec-2020
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Berkani NBellatreche LKhouri SOrdonez C(2020)The contribution of linked open data to augment a traditional data warehouseJournal of Intelligent Information Systems10.1007/s10844-020-00594-w55:3(397-421)Online publication date: 19-Feb-2020
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(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: 1-Jan-2019
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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: 1-Mar-2018
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Gosain ASharma G(2017)Object-oriented dynamic complexity measures for software understandabilityInnovations in Systems and Software Engineering10.1007/s11334-017-0304-313:2-3(177-190)Online publication date: 1-Sep-2017
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