Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/42352

TitleData warehousing in big data: from multidimensional to tabular data models
Author(s)Santos, Maribel Yasmina
Costa, Carlos
KeywordsAnalytical data model
Big data
Hive
Data warehouse
Data warehousing
Issue date20-Jul-2016
PublisherAssociation for Computing Machinery
CitationSantos, Maribel Yasmina and Carlos Costa, “Data Warehousing in Big Data: From Multidimensional to Tabular Data Models”, Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering (C3S2E), ICPS (ACM), ISBN 978-1-4503-4075-5, July 20-22, Porto, Portugal, 2016 (DOI: 10.1145/2948992.2949024)
Abstract(s)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.
TypeConference paper
URIhttp://hdl.handle.net/1822/42352
ISBN978-1-4503-4075-5
DOI10.1145/2948992.2949024
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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