Welcome to the InfoSci Platform
Reference Hub4
A Framework for Evaluating Design Methodologies for Big Data Warehouses: Measurement of the Design Process

A Framework for Evaluating Design Methodologies for Big Data Warehouses: Measurement of the Design Process

Francesco Di Tria, Ezio Lefons, Filippo Tangorra
Copyright: © 2018 |Volume: 14 |Issue: 1 |Pages: 25
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781522542643|DOI: 10.4018/IJDWM.2018010102
Cite Article Cite Article

MLA

Di Tria, Francesco, et al. "A Framework for Evaluating Design Methodologies for Big Data Warehouses: Measurement of the Design Process." IJDWM vol.14, no.1 2018: pp.15-39. http://doi.org/10.4018/IJDWM.2018010102

APA

Di Tria, F., Lefons, E., & Tangorra, F. (2018). A Framework for Evaluating Design Methodologies for Big Data Warehouses: Measurement of the Design Process. International Journal of Data Warehousing and Mining (IJDWM), 14(1), 15-39. http://doi.org/10.4018/IJDWM.2018010102

Chicago

Di Tria, Francesco, Ezio Lefons, and Filippo Tangorra. "A Framework for Evaluating Design Methodologies for Big Data Warehouses: Measurement of the Design Process," International Journal of Data Warehousing and Mining (IJDWM) 14, no.1: 15-39. http://doi.org/10.4018/IJDWM.2018010102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

This article describes how the evaluation of modern data warehouses considers new solutions adopted for facing the radical changes caused by the necessity of reducing the storage volume, while increasing the velocity in multidimensional design and data elaboration, even in presence of unstructured data that are useful for providing qualitative information. The aim is to set up a framework for the evaluation of the physical and methodological characteristics of a data warehouse, realized by considering the factors that affect the data warehouse's lifecycle when taking into account the Big Data issues (Volume, Velocity, Variety, Value, and Veracity). The contribution is the definition of a set of criteria for classifying Big Data Warehouses on the basis of their methodological characteristics. Based on these criteria, the authors defined a set of metrics for measuring the quality of Big Data Warehouses in reference to the design specifications. They show through a case study how the proposed metrics are able to check the eligibility of methodologies falling in different classes in the Big Data context.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.