Triantafillou, Peter (2018) Towards intelligent distributed data systems for scalable efficient and accurate analytics. In: 38 IEEE International Conference on Distributed Computing Systems, ICDCS, Vienna, Austria, 2-5 Jul 2018. Published in: 38 IEEE International Conference on Distributed Computing Systems, ICDCS (In Press)

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Abstract

Large analytics tasks are currently executed over Big Data Analytics Stacks (BDASs) which comprise a number of distributed systems as layers for back-end storage management, resource management, distributed/parallel execution frameworks, etc. In the current state of the art, the processing of analytical queries is too expensive, accessing large numbers of data server nodes where data is stored, crunching and transferring large volumes of data and thus consuming too many system resources, taking too much time, and failing scalability desiderata.

With this vision paper we wish to push the research envelope, offering a drastically different view of ana- lytics processing in the big data era. The radical new idea is to process analytics tasks employing learned models of data and queries, instead of accessing any base data – we call this data-less big data analytics processing. We put forward the basic principles for designing the next generation intelligent data system infrastructures realizing this new analytics-processing paradigm and present a number of specific research challenges that will take us closer to realizing the vision, which are based on the harmonic symbiosis of statistical and machine learning models with traditional system techniques. We offer a plausible research program that can address said challenges and offers preliminary ideas towards their solution. En route, we describe initial successes we have had recently with achieving scalabil- ity, efficiency, and accuracy for specific analytical tasks, substantiating the potential of the new paradigm.

Item Type:	Conference Item (Paper)
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Big Data -- Distributed processing, Client/server computing
Journal or Publication Title:	38 IEEE International Conference on Distributed Computing Systems, ICDCS
Publisher:	IEEE
Official Date:	4 April 2018
Dates:	Date Event 4 April 2018 Accepted
Date of first compliant deposit:	4 May 2018
Status:	Peer Reviewed
Publication Status:	In Press
Access rights to Published version:	Restricted or Subscription Access
Conference Paper Type:	Paper
Title of Event:	38 IEEE International Conference on Distributed Computing Systems, ICDCS
Type of Event:	Conference
Location of Event:	Vienna, Austria
Date(s) of Event:	2-5 Jul 2018
Related URLs:	Publisher

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