Title :
Alexandria: Extensible Framework for Rapid Exploration of Social Media
Author :
Heath, Fenno F. ; Hull, Richard ; Khabiri, Elham ; Riemer, Matthew ; Sukaviriya, Noi ; Vaculin, Roman
Author_Institution :
IBM Res., Yorktown Heights, NY, USA
Abstract :
The Alexandria system under development at IBM Research provides an extensible framework and platform for supporting a variety of big-data analytics and visualizations. The system is currently focused on enabling rapid exploration of text-based social media data. The system provides tools to help with constructing "domain models" (i.e., Families of keywords and extractors to enable focus on tweets and other social media documents relevant to a project), to rapidly extract and segment the relevant social media and its authors, to apply further analytics (such as finding trends and anomalous terms), and visualizing the results. The system architecture is centered around a variety of REST-based service APIs to enable flexible orchestration of the system capabilities, these are especially useful to support knowledge-worker driven iterative exploration of social phenomena. The architecture also enables rapid integration of Alexandria capabilities with other social media analytics system, as has been demonstrated through an integration with IBM Research\´s SystemG. This paper describes a prototypical usage scenario for Alexandria, along with the architecture and key underlying analytics.
Keywords :
Big Data; data analysis; data visualisation; social networking (online); text analysis; Alexandria system; IBM Research SystemG; REST-based service API; big-data analytics; big-data visualization; domain model; extensible framework; knowledge-worker driven iterative exploration; rapid text-based social media data exploration; social media documents; social media extraction; social media segmentation; tweets; Analytical models; Data visualization; Government; Indexes; Media; Twitter; analytics exploration; analytics process management; social media analytics; text analytics;
Conference_Titel :
Big Data (BigData Congress), 2015 IEEE International Congress on
Conference_Location :
New York, NY
Print_ISBN :
978-1-4673-7277-0
DOI :
10.1109/BigDataCongress.2015.77