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Big Data: from Hype to Reality?

Published: 02 June 2014 Publication History

Abstract

The traditional world of relational databases and enterprise data warehouses is being challenged by growth in data volumes, the rise of unstructured and semi-structured data, and the desire to extract more valuable business insights. In order to remain competitive: we are entering the world of 'BIG DATA'. Scale-out, commodity hardware-based solutions based on the map-reduce programming model for parallel processing on large hardware are emerging to address these BIG DATA requirements that have challenged traditional technologies. The focus of this talk is on the potential business value to be created in this area by describing the opportunities and risks arising from the recent emergence of BIG DATA Analytics technology for companies. The role businesses can play in BIG DATA is also under discussion, and finally Telefonica's experience is explained in applying BIG DATA technology, both internally for enhancement of its own business processes and externally, where we are applying the technology to benefit our customers directly.

Cited By

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  • (2021)Big Data Clustering Techniques: Recent Advances and SurveyMachine Learning and Data Mining for Emerging Trend in Cyber Dynamics10.1007/978-3-030-66288-2_3(57-79)Online publication date: 2-Apr-2021
  • (2018)Towards the Big Data in Official StatisticsProceedings of the 2nd International Conference on Computer Science and Application Engineering10.1145/3207677.3278079(1-5)Online publication date: 22-Oct-2018
  • (2016)Big DataEffective Big Data Management and Opportunities for Implementation10.4018/978-1-5225-0182-4.ch001(1-24)Online publication date: 2016
  • Show More Cited By

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Published In

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WIMS '14: Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14)
June 2014
506 pages
ISBN:9781450325387
DOI:10.1145/2611040
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

In-Cooperation

  • Aristotle University of Thessaloniki

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 June 2014

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Author Tags

  1. Analytics
  2. Big Data
  3. Business

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  • Research-article
  • Research
  • Refereed limited

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WIMS '14

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WIMS '14 Paper Acceptance Rate 41 of 90 submissions, 46%;
Overall Acceptance Rate 140 of 278 submissions, 50%

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Cited By

View all
  • (2021)Big Data Clustering Techniques: Recent Advances and SurveyMachine Learning and Data Mining for Emerging Trend in Cyber Dynamics10.1007/978-3-030-66288-2_3(57-79)Online publication date: 2-Apr-2021
  • (2018)Towards the Big Data in Official StatisticsProceedings of the 2nd International Conference on Computer Science and Application Engineering10.1145/3207677.3278079(1-5)Online publication date: 22-Oct-2018
  • (2016)Big DataEffective Big Data Management and Opportunities for Implementation10.4018/978-1-5225-0182-4.ch001(1-24)Online publication date: 2016
  • (2015)Linked crowdsourced data - Enabling location analytics in the linking open data cloudProceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)10.1109/ICOSC.2015.7050776(40-48)Online publication date: Feb-2015

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