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

Klaus Ackermann; Simon D. Angus

doi:10.1016/j.procs.2014.05.220

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

Klaus Ackermann, Simon D. Angus

Department of Economics

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

27 Citations (Scopus)

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 language	English
Title of host publication	2014 International Conference on Computational Science (ICCS 2014)
Editors	David Abramson, Michael Lees, Valeria V. Krzhizhanovskaya, Jack Dongarra, Peter M.A. Sloot
Place of Publication	Amsterdam Netherlands
Publisher	Elsevier
Pages	2360-2369
Number of pages	10
DOIs	https://doi.org/10.1016/j.procs.2014.05.220
Publication status	Published - 2014
Event	International Conference on Computational Science 2014 - Cairns, Australia Duration: 10 Jun 2014 → 12 Jun 2014 Conference number: 14th http://www.iccs-meeting.org/iccs2014/

Conference

Conference	International Conference on Computational Science 2014
Abbreviated title	ICCS 2014
Country/Territory	Australia
City	Cairns
Period	10/06/14 → 12/06/14
Internet address	http://www.iccs-meeting.org/iccs2014/

Keywords

Big data
Social sciences
Internet census
GIS
Memory reduction

Access to Document

10.1016/j.procs.2014.05.220Licence: CC BY

225598228-oaFinal published version, 12 MBLicence: CC BY

Cite this

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

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.",

keywords = "Big data, Social sciences, Internet census, GIS, Memory reduction",

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year = "2014",

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booktitle = "2014 International Conference on Computational Science (ICCS 2014)",

publisher = "Elsevier",

address = "Netherlands",

note = "International Conference on Computational Science 2014, ICCS 2014 ; Conference date: 10-06-2014 Through 12-06-2014",

url = "http://www.iccs-meeting.org/iccs2014/",

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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). Elsevier, Amsterdam Netherlands, pp. 2360-2369, International Conference on Computational Science 2014, Cairns, Queensland, 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.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AB - 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.

KW - Big data

KW - Social sciences

KW - Internet census

KW - GIS

KW - Memory reduction

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DO - 10.1016/j.procs.2014.05.220

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EP - 2369

BT - 2014 International Conference on Computational Science (ICCS 2014)

A2 - Abramson, David

A2 - Lees, Michael

A2 - Krzhizhanovskaya, Valeria V.

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PB - Elsevier

CY - Amsterdam Netherlands

Y2 - 10 June 2014 through 12 June 2014

ER -

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

Abstract

Conference

Keywords

Access to Document

Other files and links

Australian Business Deans Council (ABDC) Award for Innovation and Excellence in Research

Australian Synchrotron

MASSIVE

Cite this

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

Abstract

Conference

Keywords

Access to Document

Other files and links

Prizes

Australian Business Deans Council (ABDC) Award for Innovation and Excellence in Research

Equipment

Australian Synchrotron

MASSIVE

Cite this