DG_Summ: A Schema-Driven Approach for personalized Summarizing Heterogeneous Data Graphs

Amal Beldi1, 2, Salma Sassi2, Richard Chbeir2 and Abderrazek Jemai1, 3

  1. Tunis El Manar University, Faculty of Mathematical Physical and Natural Sciences of Tunis
    SERCOM Laboratory, 1068 Tunis, Tunisia
  2. University Pau & Pays Adour
    LIUPPA, Anglet, 64600, France
    salma.tissaoui@univ-pau.fr, richard.chbeir@univ-pau.fr
  3. Carthage University, Polytechnic School of Tunisia,
    SERCOM Laboratory, INSAT, 1080, Tunis, Tunisia


Advances in computing resources have enabled the processing of vast amounts of data. However, identifying trends in such data remains challenging for humans, especially in fields like medicine and social networks. These challenges make it difficult to process, analyze, and visualize the data. In this context, graph summarization has emerged as an effective framework aiming to facilitate the identification of structure and meaning in data. The problem of graph summarization has been studied in the literature and many approaches for static contexts are proposed to summarize the graph. These approaches provide a compressed version of the graph that removes many details while retaining its essential structure. However, they are computationally prohibitive and do not scale to large graphs in terms of both structure and content. Additionally, there is no framework providing summarization of mixed sources with the goal of creating a dynamic, syntactic, and semantic data summary. In this paper, our key contribution is focused on modeling data graphs, summarizing data from multiple sources using a schema-driven approach, and visualizing the graph summary version according to the needs of each user. We demonstrate this approach through a case study on the use of the E-health domain.

Key words

Heterogenous data, labeled graph, Graph summarization,operation, structure, content, versioning

Digital Object Identifier (DOI)


Publication information

Volume 20, Issue 4 (September 2023)
Year of Publication: 2023
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Beldi, A., Sassi, S., Chbeir, R., Jemai, A.: DG_Summ: A Schema-Driven Approach for personalized Summarizing Heterogeneous Data Graphs. Computer Science and Information Systems, Vol. 20, No. 4. (2023), https://doi.org/10.2298/CSIS230331062B