Article Version 1 Preserved in Portico This version is not peer-reviewed
Modeling Analytical Streams for Social Business Intelligence
Version 1 : Received: 25 June 2018 / Approved: 26 June 2018 / Online: 26 June 2018 (12:48:17 CEST)
A peer-reviewed article of this Preprint also exists.
Lanza-Cruz, I.; Berlanga, R.; Aramburu, M.J. Modeling Analytical Streams for Social Business Intelligence. Informatics 2018, 5, 33. Lanza-Cruz, I.; Berlanga, R.; Aramburu, M.J. Modeling Analytical Streams for Social Business Intelligence. Informatics 2018, 5, 33.
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 contents and the company 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.
social business intelligence; data streaming models; linked data
MATHEMATICS & COMPUTER SCIENCE, Information Technology & Data Management
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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