An Analytics-Aware Conceptual Model for Evolving Graphs

  • Amine Ghrab
  • Sabri Skhiri
  • Salim Jouili
  • Esteban Zimányi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8057)


Graphs are ubiquitous data structures commonly used to represent highly connected data. Many real-world applications, such as social and biological networks, are modeled as graphs. To answer the surge for graph data management, many graph database solutions were developed. These databases are commonly classified as NoSQL graph databases, and they provide better support for graph data management than their relational counterparts. However, each of these databases implement their own operational graph data model, which differ among the products. Further, there is no commonly agreed conceptual model for graph databases.

In this paper, we introduce a novel conceptual model for graph databases. The aim of our model is to provide analysts with a set of simple, well-defined, and adaptable conceptual components to perform rich analysis tasks. These components take into account the evolving aspect of the graph. Our model is analytics-oriented, flexible and incremental, enabling analysis over evolving graph data. The proposed model provides a typing mechanism for the underlying graph, and formally defines the minimal set of data structures and operators needed to analyze the graph.


Data Warehousing Graph Data Input Graph Graph Database Multidimensional Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  • Amine Ghrab
    • 1
    • 2
  • Sabri Skhiri
    • 1
  • Salim Jouili
    • 1
  • Esteban Zimányi
    • 2
  1. 1.Eura Nova R&DMont-Saint-GuibertBelgium
  2. 2.Université Libre de BruxellesBelgium

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