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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: 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) ISSN 2575-8411. doi:10.1109/ICDCS.2018.00119
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Official URL: https://doi.org/10.1109/ICDCS.2018.00119
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 analytics 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 scalability, efficiency, and accuracy for specific analytical tasks, substantiating the potential of the new paradigm.
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: | 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 2575-8411 | ||||||
| Official Date: | 23 July 2018 | ||||||
| Dates: |
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| DOI: | 10.1109/ICDCS.2018.00119 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| 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 | ||||||
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