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Tuning small analytics on Big Data: Data partitioning and secondary indexes in the Hadoop ecosystem

Author
Romero, O.; Herrero, V.; Abello, A.; Ferrarons, Jaume
Type of activity
Journal article
Journal
Information systems
Date of publication
2015-12
Volume
54
First page
336
Last page
356
DOI
https://doi.org/10.1016/j.is.2014.09.005 Open in new window
Project funding
Desarrollo de nuevas tecnicas y herramientas para la integracion de informacion
Repository
http://hdl.handle.net/2117/78953 Open in new window
URL
http://www.sciencedirect.com/science/article/pii/S0306437914001458 Open in new window
Abstract
In the recent years the problems of using generic storage (i.e., relational) techniques for very specific applications have been detected and outlined and, as a consequence, some alternatives to Relational DBMSs (e.g., HBase) have bloomed. Most of these alternatives sit on the cloud and benefit from cloud computing, which is nowadays a reality that helps us to save money by eliminating the hardware as well as software fixed costs and just pay per use. On top of this, specific querying frameworks...
Citation
Romero, O., Herrero, V., Abelló, A., Ferrarons, Jaume. Tuning small analytics on Big Data: Data partitioning and secondary indexes in the Hadoop ecosystem. "Information systems", Desembre 2015, vol. 54, p. 336-356.
Keywords
Big Data, Cost estimation, Indexes, Multidimensional model, OLAP, Partitioning
Group of research
DTIM - Database Technologies and lnformation Management Group
IMP - Information Modelling and Processing
inLab FIB
inSSIDE - integrated Software, Service, Information and Data Engineering

Participants

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