  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.
