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Collaborative Business Intelligence

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Business Intelligence (eBISS 2011)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 96))

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Summary

The idea of collaborative BI is to extend the decision-making process beyond the company boundaries thanks to cooperation and data sharing with other companies and organizations. Unfortunately, traditional BI applications are aimed at serving individual companies, and they cannot operate over networks of companies characterized by an organizational, lexical, and semantic heterogeneity. In such distributed business scenarios, to maximize the effectiveness of monitoring and decision making processes there is a need for innovative approaches and architectures. Data warehouse integration is an enabling technique for collaborative BI, and has been investigated along three main directions: warehousing approaches, where the integrated data are physically materialized, federative approaches, where the integration is virtual and based on a global schema, and peer-to-peer approaches, that do not rely on a global schema to integrate the component data warehouses. In this paper we explore and compare these three directions by surveying the available work in the literature. Then we outline a new peer-to-peer framework, called Business Intelligence Network, where peers expose querying functionalities aimed at sharing business information for the decision-making process. The main features of this framework are decentralization, scalability, and full autonomy of peers.

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Author information

Authors and Affiliations

  1. Department of Electronics, Computer Sciences and Systems (DEIS), University of Bologna, Bologna, Italy

    Stefano Rizzi

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  1. Stefano Rizzi
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Editors and Affiliations

  1. MAS Laboratory, Ecole Centrale Paris, 92 295, Châtenay-Malabry Cedex, France

    Marie-Aude Aufaure

  2. Department of Computer & Decision Engineering (CoDE) CP 165/15, Universite Libre de Bruxelles, 1050, Brussels, Belgium

    Esteban Zimányi

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Rizzi, S. (2012). Collaborative Business Intelligence. In: Aufaure, MA., Zimányi, E. (eds) Business Intelligence. eBISS 2011. Lecture Notes in Business Information Processing, vol 96. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27358-2_9

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  • DOI: https://doi.org/10.1007/978-3-642-27358-2_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27357-5

  • Online ISBN: 978-3-642-27358-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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