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Design Issues in Social Business Intelligence Projects

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Business Intelligence (eBISS 2015)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 253))

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Abstract

With the term Social Business Intelligence we refer to a branch of Business Intelligence specialized in applying On-Line Analytical Processing analysis to User-Generated Contents collected from the Web and other sources of social information. The high dynamics of the domain as well as the nature of the source data, that are textual rather than numerical, require specific techniques both for modeling data and managing a project. Despite the increasing diffusion of Social Business Intelligence applications, only few works in the academic literature addressed such distinguishing features. In this paper we propose both a modeling technique and a methodology that enable the possibility of carrying out a more dynamic and expressive design in Social Business Intelligence projects. We also propose a set of experimental results on real data and real projects proving the effectiveness of our solutions.

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Notes

  1. 1.

    In the literature the term Social BI is also used to define the collaborative development of post user-generated analytics among business analysts and data mining professionals.

  2. 2.

    Though the roll-up partial order between levels is part of the hierarchy schema, its historicization is handled at the instance level in both stars and meta-stars; while from the extensional point of view inter-level relationships can be reconstructed from the relationships between level members, from the intensional point of view they are explicitly stored only in meta-data repositories, not in dimension table schemata.

  3. 3.

    Evaluating sentiment analysis results is a difficult task since they may change a lot depending on the clip domain, the type of sources considered, etc. [23]. Nonetheless a reference value for sentiment analysis accuracy on simple domains is around \(60\,\% - 70\,\%\). Please consider, that a group of human experts typically find an agreement on the sentiment in the \(80\,\%\) of cases.

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Authors and Affiliations

  1. DISI, University of Bologna, Bologna, Italy

    Matteo Golfarelli

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  1. Matteo Golfarelli
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Correspondence to Matteo Golfarelli .

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Editors and Affiliations

  1. Dept. of Computer and Decision Engg., Universite Libre de Bruxelles, Brussels, Belgium

    Esteban Zimányi

  2. Database Techn. & Inf. Management Group, Universitat Politecnica de Cataluny, Barcelona, Spain

    Alberto Abelló

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Golfarelli, M. (2016). Design Issues in Social Business Intelligence Projects. In: Zimányi, E., Abelló, A. (eds) Business Intelligence. eBISS 2015. Lecture Notes in Business Information Processing, vol 253. Springer, Cham. https://doi.org/10.1007/978-3-319-39243-1_3

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