Concise essence-preserving big data representation

Philip Derbeko, Shlomi Dolev, Ehud Gudes, Jeffrey D. Ullman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Controversially, more data is not necessary better than less data. The explosion of the data lead to a number of interesting practical and theoretical problems. Among those problems are the need to filter, process, verify, index, distribute, protect and make redundant copies of the data. This data 'massaging' usually take a lot of time and processing power. However, the quantity of the collected data does not necessary mean quality, as a lot of data is repetitive or does not contain any new information. Nevertheless, it still has to be processed, filtered, consumes high communication volume, has to be protected from breaches and from storage failures. In this position paper we propose to perform data reduction techniques on the collected (big) data prior to gathering of the data in a single location. In many cases (exemplified by two use-cases), especially in Internet-of-Things (IoT), those techniques might save tremendous amounts of power, processing time and network traffic.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3662-3665
Number of pages4
ISBN (Electronic)9781467390040
DOIs
StatePublished - 1 Jan 2016
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: 5 Dec 20168 Dec 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

Conference

Conference4th IEEE International Conference on Big Data, Big Data 2016
Country/TerritoryUnited States
CityWashington
Period5/12/168/12/16

Keywords

  • Big Data
  • Big Data Analysis
  • Big Data Performance
  • Data Reduction

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