Graph integration of structured, semistructured and unstructured data for data journalism

• URL: http://arxiv.org/abs/2012.08830v1
• Date: Wed, 16 Dec 2020 09:59:27 GMT
• Title: Graph integration of structured, semistructured and unstructured data for data journalism
• Authors: Angelos-Christos Anadiotis, Oana Balalau, Catarina Conceicao, Helena Galhardas, Mhd Yamen Haddad, Ioana Manolescu, Tayeb Merabti, Jingmao You
• Abstract summary: We describe a complete approach for integrating dynamic sets of heterogeneous datasets. Our approach is implemented within the ConnectionLens system; we validate it through a set of experiments.
• Score: 4.508924138721326
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Digital data is a gold mine for modern journalism. However, datasets which interest journalists are extremely heterogeneous, ranging from highly structured (relational databases), semi-structured (JSON, XML, HTML), graphs (e.g., RDF), and text. Journalists (and other classes of users lacking advanced IT expertise, such as most non-governmental-organizations, or small public administrations) need to be able to make sense of such heterogeneous corpora, even if they lack the ability to define and deploy custom extract-transform-load workflows, especially for dynamically varying sets of data sources. We describe a complete approach for integrating dynamic sets of heterogeneous datasets along the lines described above: the challenges we faced to make such graphs useful, allow their integration to scale, and the solutions we proposed for these problems. Our approach is implemented within the ConnectionLens system; we validate it through a set of experiments.

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