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Large-scale social-media analytics on stratosphere

Boden, Christoph; Karnstedt, Marcel; Fernández, Miriam and Volker, Markl (2013). Large-scale social-media analytics on stratosphere. In: 22nd International World Wide Web Conference (WWW 2013), 13-17 May 2014, Rio de Janeiro, Brazil.

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Abstract

The importance of social-media platforms and online communities - in business as well as public context - is more and more acknowledged and appreciated by industry and researchers alike. Consequently, a wide range of analytics has been proposed to understand, steer, and exploit the mechanics and laws driving their functionality and creating the resulting benefits. However, analysts usually face significant problems in scaling existing and novel approaches to match the data volume and size of modern online communities. In this work, we propose and demonstrate the usage of the massively parallel data processing system Stratosphere, based on second order functions as an extended notion of the MapReduce paradigm, to provide a new level of scalability to such social-media analytics. Based on the popular example of role analysis, we present and illustrate how this massively parallel approach can be leveraged to scale out complex data-mining tasks, while providing a programming approach that eases the formulation of complete analytical workflows.

Item Type: Conference or Workshop Item
Copyright Holders: International World Wide Web Conference Committee (IW3C2).
Project Funding Details:
Funded Project NameProject IDFunding Body
ROBUST257859EU
Not SetFOR1036German Research Foundation
RADAR01ISI2033German Federal Ministry of Education and Research
Not SetNot SetEuropean Institute of Innovation and Technology
Keywords: role analysis; behaviour analysis; online communities; scalability; Stratosphere; community analysis; Boards.ie
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 41397
Depositing User: Miriam Fernandez
Date Deposited: 25 Nov 2014 15:26
Last Modified: 09 Dec 2018 00:30
URI: http://oro.open.ac.uk/id/eprint/41397
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