A resource efficient big data analysis method for the social sciences: The case of global IP activity

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

Abstract

This paper presents a novel and efficient way of analysing big datasets used in social science research. We provide and demonstrate a way to deal with such datasets without the need for high performance distributed computational facilities. Using an Internet census dataset and with the help of freely available tools and programming libraries, we visualize global IP activity in a spatial and time dimension. We observe a considerable reduction in storage size of our dataset coupled with a faster processing time.
Original languageEnglish
Title of host publication2014 International Conference on Computational Science (ICCS 2014)
EditorsDavid Abramson, Michael Lees, Valeria V. Krzhizhanovskaya, Jack Dongarra, Peter M.A. Sloot
Place of PublicationAmsterdam Netherlands
PublisherElsevier
Pages2360-2369
Number of pages10
DOIs
Publication statusPublished - 2014
EventInternational Conference on Computational Science 2014: Big Data meets Computational Science - Cairns, Australia
Duration: 10 Jun 201412 Jun 2014
Conference number: 14
http://www.iccs-meeting.org/iccs2014/

Publication series

NameProcedia Computer Science
PublisherElsevier
ISSN (Electronic)1877-0509

Conference

ConferenceInternational Conference on Computational Science 2014
Abbreviated titleICCS 2014
CountryAustralia
CityCairns
Period10/06/1412/06/14
OtherThe International Conference on Computational Science is an annual conference that brings together researchers and scientists from mathematics and computer science as basic computing disciplines, researchers from various application areas who are pioneering computational methods in sciences such as physics, chemistry, life sciences, and engineering, as well as in arts and humanitarian fields, to discuss problems and solutions in the area, to identify new issues, and to shape future directions for research.

ICCS 2014 in Cairns, Queensland, will be the fourteenth in this series of highly successful conferences.
Internet address

Keywords

  • Big data
  • Social sciences
  • Internet census
  • GIS
  • Memory reduction

Cite this

Ackermann, K., & Angus, S. D. (2014). A resource efficient big data analysis method for the social sciences: The case of global IP activity. In D. Abramson, M. Lees, V. V. Krzhizhanovskaya, J. Dongarra, & P. M. A. Sloot (Eds.), 2014 International Conference on Computational Science (ICCS 2014) (pp. 2360-2369). (Procedia Computer Science). Amsterdam Netherlands: Elsevier. Procedia Computer Science https://doi.org/10.1016/j.procs.2014.05.220
Ackermann, Klaus ; Angus, Simon D. / A resource efficient big data analysis method for the social sciences : The case of global IP activity. 2014 International Conference on Computational Science (ICCS 2014). editor / David Abramson ; Michael Lees ; Valeria V. Krzhizhanovskaya ; Jack Dongarra ; Peter M.A. Sloot. Amsterdam Netherlands : Elsevier, 2014. pp. 2360-2369 (Procedia Computer Science). (Procedia Computer Science).
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abstract = "This paper presents a novel and efficient way of analysing big datasets used in social science research. We provide and demonstrate a way to deal with such datasets without the need for high performance distributed computational facilities. Using an Internet census dataset and with the help of freely available tools and programming libraries, we visualize global IP activity in a spatial and time dimension. We observe a considerable reduction in storage size of our dataset coupled with a faster processing time.",
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Ackermann, K & Angus, SD 2014, A resource efficient big data analysis method for the social sciences: The case of global IP activity. in D Abramson, M Lees, VV Krzhizhanovskaya, J Dongarra & PMA Sloot (eds), 2014 International Conference on Computational Science (ICCS 2014). Procedia Computer Science, Elsevier, Amsterdam Netherlands, Procedia Computer Science, pp. 2360-2369, International Conference on Computational Science 2014, Cairns, Australia, 10/06/14. https://doi.org/10.1016/j.procs.2014.05.220

A resource efficient big data analysis method for the social sciences : The case of global IP activity. / Ackermann, Klaus; Angus, Simon D.

2014 International Conference on Computational Science (ICCS 2014). ed. / David Abramson; Michael Lees; Valeria V. Krzhizhanovskaya; Jack Dongarra; Peter M.A. Sloot. Amsterdam Netherlands : Elsevier, 2014. p. 2360-2369 (Procedia Computer Science).

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

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Ackermann K, Angus SD. A resource efficient big data analysis method for the social sciences: The case of global IP activity. In Abramson D, Lees M, Krzhizhanovskaya VV, Dongarra J, Sloot PMA, editors, 2014 International Conference on Computational Science (ICCS 2014). Amsterdam Netherlands: Elsevier. 2014. p. 2360-2369. (Procedia Computer Science). (Procedia Computer Science). https://doi.org/10.1016/j.procs.2014.05.220