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Open AccessArticle

Modeling Analytical Streams for Social Business Intelligence

1
Department de Llenguatges i Sistemes Informàtics, Universitat Jaume I, 12071 Castelló de la Plana, Spain
2
Department de’Enginyeria i Ciència dels Computadors, Universitat Jaume I, 12071 Castelló de la Plana, Spain
*
Author to whom correspondence should be addressed.
Informatics 2018, 5(3), 33; https://doi.org/10.3390/informatics5030033
Received: 25 June 2018 / Revised: 23 July 2018 / Accepted: 23 July 2018 / Published: 1 August 2018
(This article belongs to the Special Issue Data Modeling for Big Data Analytics)
Social Business Intelligence (SBI) enables companies to capture strategic information from public social networks. Contrary to traditional Business Intelligence (BI), SBI has to face the high dynamicity of both the social network’s contents and the company’s analytical requests, as well as the enormous amount of noisy data. Effective exploitation of these continuous sources of data requires efficient processing of the streamed data to be semantically shaped into insightful facts. In this paper, we propose a multidimensional formalism to represent and evaluate social indicators directly from fact streams derived in turn from social network data. This formalism relies on two main aspects: the semantic representation of facts via Linked Open Data and the support of OLAP-like multidimensional analysis models. Contrary to traditional BI formalisms, we start the process by modeling the required social indicators according to the strategic goals of the company. From these specifications, all the required fact streams are modeled and deployed to trace the indicators. The main advantages of this approach are the easy definition of on-demand social indicators, and the treatment of changing dimensions and metrics through streamed facts. We demonstrate its usefulness by introducing a real scenario user case in the automotive sector. View Full-Text
Keywords: Social Business Intelligence; data streaming models; linked data Social Business Intelligence; data streaming models; linked data
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Lanza-Cruz, I.; Berlanga, R.; Aramburu, M.J. Modeling Analytical Streams for Social Business Intelligence. Informatics 2018, 5, 33.

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