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Event Panning in a Stream of Big Data

Authors: 

Hugo Hromic, Marcel Karnstedt, Mengjiao Wang, Alice Hogan, Vaclav Belak, Conor Hayes

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
In this paper, we present a hands-on experience report from designing and building an architecture for preprocessing & delivering real-time social-media messages in the context of a large international sporting event. In contrast to the standard topic-centred approach, we apply social community analytics to filter, segregate and rank an incoming stream of Twitter messages for display on a mobile device. The objective is to provide the user with a “breaking news” summary of the main sources, events and messages discovered. The architecture can be generally deployed in any context where (mobile) information consumers need to keep track of the latest news & trends and the corresponding sources in a stream of Big Data. We describe the complete infrastructure and the fresh stance we took for the analytics, the lessons we learned while developing it, as well as the main challenges and open issues we identified.
Conference Name: 
LWA Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML)
Digital Object Identifer (DOI): 
10.NA
Publication Date: 
12/09/2012
Conference Location: 
Germany
Research Group: 
Institution: 
National University of Ireland, Galway (NUIG)
Open access repository: 
Yes