  Investigative Journalism (IJ, in short) is staple of modern, democratic
societies. IJ often necessitates working with large, dynamic sets of
heterogeneous, schema-less data sources, which can be structured,
semi-structured, or textual, limiting the applicability of classical data
integration approaches. In prior work, we have developed ConnectionLens, a
system capable of integrating such sources into a single heterogeneous graph,
leveraging Information Extraction (IE) techniques; users can then query the
graph by means of keywords, and explore query results and their neighborhood
using an interactive GUI. Our keyword search problem is complicated by the
graph heterogeneity, and by the lack of a result score function that would
allow to prune some of the search space. In this work, we describe an actual IJ
application studying conflicts of interest in the biomedical domain, and we
show how ConnectionLens supports it. Then, we present novel techniques
addressing the scalability challenges raised by this application: one allows to
reduce the significant IE costs while building the graph, while the other is a
novel, parallel, in-memory keyword search engine, which achieves orders of
magnitude speed-up over our previous engine. Our experimental study on the
real-world IJ application data confirms the benefits of our contributions.
