OLAPing social media : the case of Twitter

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2013
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Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM '13. New York, New York, USA: ACM Press, 2013, pp. 1139-1146. ISBN 978-1-4503-2240-9. Available under: doi: 10.1145/2492517.2500273
Zusammenfassung

Social networks are platforms where millions of users interact frequently and share variety of digital content with each other. Users express their feelings and opinions on every topic of interest. These opinions carry import value for personal, academic and commercial applications, but the volume and the speed at which these are produced make it a challenging task for researchers and the underlying technologies to provide useful insights to such data. We attempt to extend the established OLAP(On-line Analytical Processing) technology to allow multidimensional analysis of social media data by integrating text and opinion mining methods into the data warehousing system and by exploiting various knowledge discovery techniques to deal with semi-structured and unstructured data from social media.



The capabilities of OLAP are extended by semantic enrichment of the underlying dataset to discover new measures and dimensions for building data cubes and by supporting up-to-date analysis of the evolving as well as the historical social media data. The benefits of such an analysis platform are demonstrated by building a data warehouse for a social network of Twitter, dynamically enriching the underlying dataset and enabling multidimensional analysis.

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Fachgebiet (DDC)
004 Informatik
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Data Warehousing, OLAP, Business Intelligence, Twitter, Modeling
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the 2013 IEEE/ACM International Conference, 25. Aug. 2013 - 28. Aug. 2013, Niagara, Ontario, Canada
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ISO 690REHMAN, Nafees Ur, Andreas WEILER, Marc H. SCHOLL, 2013. OLAPing social media : the case of Twitter. the 2013 IEEE/ACM International Conference. Niagara, Ontario, Canada, 25. Aug. 2013 - 28. Aug. 2013. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM '13. New York, New York, USA: ACM Press, 2013, pp. 1139-1146. ISBN 978-1-4503-2240-9. Available under: doi: 10.1145/2492517.2500273
BibTex
@inproceedings{Rehman2013OLAPi-25763,
  year={2013},
  doi={10.1145/2492517.2500273},
  title={OLAPing social media : the case of Twitter},
  isbn={978-1-4503-2240-9},
  publisher={ACM Press},
  address={New York, New York, USA},
  booktitle={Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM '13},
  pages={1139--1146},
  author={Rehman, Nafees Ur and Weiler, Andreas and Scholl, Marc H.}
}
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    <dcterms:abstract xml:lang="eng">Social networks are platforms where millions of users interact frequently and share variety of digital content with each other. Users express their feelings and opinions on every topic of interest. These opinions carry import value for personal, academic and commercial applications, but the volume and the speed at which these are produced make it a challenging task for researchers and the underlying technologies to provide useful insights to such data. We attempt to extend the established OLAP(On-line Analytical Processing) technology to allow multidimensional analysis of social media data by integrating text and opinion mining methods into the data warehousing system and by exploiting various knowledge discovery techniques to deal with semi-structured and unstructured data from social media.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;The capabilities of OLAP are extended by semantic enrichment of the underlying dataset to discover new measures and dimensions for building data cubes and by supporting up-to-date analysis of the evolving as well as the historical social media data. The benefits of such an analysis platform are demonstrated by building a data warehouse for a social network of Twitter, dynamically enriching the underlying dataset and enabling multidimensional analysis.</dcterms:abstract>
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