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SocialGlue: a Pluggable, Scalable, and Multi-Platform Service for Data Analysis

Authors:
Stefano Amico
Marika Cappai
Salvatore Carta
Luca Mancosu
Fabrizio Mulas
Maria Luisa Mulas
Paolo Pilloni
Giordano Sini

Keywords: Social Web; Computational Services; Framework Architecture.

Abstract:
The growing use of Social Network Sites (SNS) and the exponential growth of social data is opening new research challenges in several scientific domains, such as: Recommender Systems, Data Mining, Sentiment Analysis, and Human-Computer Interaction, just to name a few. In this scenario, researchers from different fields are facing several obstacles given the lack of highly customizable frameworks able to let them perform a large number of ad hoc studies of big data flows coming from SNSs. Trying to overcome these challenges, this paper sets out Social Glue (SG), a pluggable, scalable, and multi-platform social analysis service. SG is designed to easily enable the connection to potentially any SNS allowing scientists to plug and manage the execution of their algorithms against connected SNSs seamlessly from SG with the minimum effort. In this way, researchers can focus more on the design of algorithms rather than in the software infrastructure needed to set up their experiments.

Pages: 28 to 33

Copyright: Copyright (c) IARIA, 2016

Publication date: March 20, 2016

Published in: conference

ISSN: 2308-3549

ISBN: 978-1-61208-459-6

Location: Rome, Italy

Dates: from March 20, 2016 to March 24, 2016