Special section on large-scale analytics

Special section on large-scale analytics The VLDB Journal (2012) 21:587–588 DOI 10.1007/s00778-012-0291-9 GUEST EDITORIAL Wolfgang Lehner · Michael J. Franklin Published online: 6 September 2012 © Springer-Verlag 2012 Big Data is no longer exclusively the domain of big orga- with two main contributions. First, the authors introduce the nizations. Companies, collaborations, and organizations of technique of cyclic piggybacking to implement shared scans all types and sizes are increasingly faced with the need to and reduce to overall access cost. Then, given the ability analyze and make sense of large and growing collections of to share scans, the paper addresses the optimization of job data. Solving the challenge of large-scale analytics requires scheduling to exploit the shared scans in an optimal fashion. innovation across the spectrum of data management: Large The paper then presents the circumflex scheduler, a gener- volumes of data have to be acquired, processed, stored, and alized version of the flex scheduler allowing a wide variety eventually reclaimed. Complex statistical procedures must of different cost metrics to be used. Insights into the imple- be applied to those large data sets. Transactional guarantees mentation of these techniques and results of simulation and are required to provide a consistent picture with operational real benchmark http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Special section on large-scale analytics

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Publisher
Springer-Verlag
Copyright
Copyright © 2012 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-012-0291-9
Publisher site
See Article on Publisher Site

Abstract

The VLDB Journal (2012) 21:587–588 DOI 10.1007/s00778-012-0291-9 GUEST EDITORIAL Wolfgang Lehner · Michael J. Franklin Published online: 6 September 2012 © Springer-Verlag 2012 Big Data is no longer exclusively the domain of big orga- with two main contributions. First, the authors introduce the nizations. Companies, collaborations, and organizations of technique of cyclic piggybacking to implement shared scans all types and sizes are increasingly faced with the need to and reduce to overall access cost. Then, given the ability analyze and make sense of large and growing collections of to share scans, the paper addresses the optimization of job data. Solving the challenge of large-scale analytics requires scheduling to exploit the shared scans in an optimal fashion. innovation across the spectrum of data management: Large The paper then presents the circumflex scheduler, a gener- volumes of data have to be acquired, processed, stored, and alized version of the flex scheduler allowing a wide variety eventually reclaimed. Complex statistical procedures must of different cost metrics to be used. Insights into the imple- be applied to those large data sets. Transactional guarantees mentation of these techniques and results of simulation and are required to provide a consistent picture with operational real benchmark

Journal

The VLDB JournalSpringer Journals

Published: Oct 1, 2012

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