OLAPing social media : the case of Twitter

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REHMAN, 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. the 2013 IEEE/ACM International Conference. Niagara, Ontario, Canada, 25. Aug 2013 - 28. Aug 2013. New York, New York, USA:ACM Press, pp. 1139-1146. ISBN 978-1-4503-2240-9. Available under: doi: 10.1145/2492517.2500273

@inproceedings{Rehman2013OLAPi-25763, title={OLAPing social media : the case of Twitter}, year={2013}, doi={10.1145/2492517.2500273}, isbn={978-1-4503-2240-9}, address={New York, New York, USA}, publisher={ACM Press}, 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.} }

deposit-license Rehman, Nafees Ur 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.<br /><br /><br /><br />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. Weiler, Andreas 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013) : Niagara Falls, Canada, August 25-28, 2013. - New York, NY : ACM, 2013. - S. 1139-1146. - ISBN 978-1-4503-2240-9 2013 OLAPing social media : the case of Twitter eng Scholl, Marc H. Scholl, Marc H. 2014-08-31T22:25:05Z Weiler, Andreas 2014-01-13T09:35:37Z Rehman, Nafees Ur

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