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

作者：Di Tria Francesco^*; Lefons Ezio; Tangorra Filippo

来源：International Journal of Data Warehousing and Mining, 2018, 14(1): 15-39.

DOI：10.4018/IJDWM.2018010102

• Agility
• Automation
• Big Data Warehouse
• Metrics
• Ontology

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.

• 出版日期2018-3