Paper Info
Title
Data Warehousing in Big Data: From Multidimensional to Tabular Data Models.
Abstract
Data warehouses are central pieces in business intelligence and analytics as these repositories ensure proper data storage and querying, being supported by data models that allow the analysis of data by different perspectives. Those perspectives support users and organizations in the decision-making process. In Big Data environments, Hive is used as a distributed storage mechanism that provides data warehousing capabilities. Its data schemas are defined attending to the analytical requirements specified by the users. In this work, multidimensional data models are used as the source of those requirements, allowing the automatic transformation of a multidimensional schema into a tabular schema suited to be implemented in Hive. To achieve this objective, a set of rules is proposed and tested in a demonstration case, showing the applicability and usefulness of the proposed approach.
Year
DOI
Venue
2016
10.1145/2948992.2949024
C3S2E
Field
DocType
Citations 
Data warehouse,Data transformation,Data modeling,Data quality,Data analysis,Computer science,Analytics,Business intelligence,Big data,Database
Conference
3
PageRank 
References 
Authors
0.45
5
2
Name
Order
Citations
PageRank
Maribel Yasmina Santos114635.41
Carlos Costa2389.15