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Towards NoSQL Graph Data Warehouse for Big Social Data Analysis

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Intelligent Systems Design and Applications (ISDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

Abstract

Big Data generated from social networking sites is the crude oil of this century. Data warehousing and analysing social actions and interactions can help corporations to capture opinions, suggest friends, recommend products and services and make intelligent decisions that improve customer loyalty. However, traditional data warehouses built on relational databases are unable to handle this massive amount of data. As an alternative, NoSQL (Not only Structured Query Language) databases are gaining popularity when building Big Data Warehouses. The current state of the art of proposed NoSQL data warehouses is captured and discussed in this paper. The paper will also focus on the opportunities and challenges of using NoSQL graph databases for storing and querying Big Social Data and how graph theory can help to mine information from these data warehouses.

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Author information

Authors and Affiliations

  1. Research Groups in Intelligent Machines, University of Sfax, National School of Engineers (ENIS), BP 1173, Sfax, 3038, Tunisia

    Hajer Akid & Mounir Ben Ayed

  2. Faculty of Science of Sfax, BP 1171, Sfax, 3000, Tunisia

    Hajer Akid & Mounir Ben Ayed

Authors
  1. Hajer Akid
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  2. Mounir Ben Ayed
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Corresponding author

Correspondence to Hajer Akid .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

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Akid, H., Ben Ayed, M. (2017). Towards NoSQL Graph Data Warehouse for Big Social Data Analysis. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_95

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  • DOI: https://doi.org/10.1007/978-3-319-53480-0_95

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53479-4

  • Online ISBN: 978-3-319-53480-0

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