ConnectionLens: Finding Connections Across Heterogeneous Data Sources

Abstract : Nowadays, journalism is facilitated by the existence of large amounts of publicly available digital data sources. In particular , journalists can do investigative work, which typically consists on keyword-based searches over many heterogeneous , independently produced and dynamic data sources, to obtain useful, interconnecting and traceable information. We propose to demonstrate ConnectionLens, a system based on a novel algorithm for keyword search across heterogeneous data sources. Our demonstration scenarios are based on use cases suggested by journalists from the french journal Le Monde, with whom we collaborate.
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Camille Chanial, Rédouane Dziri, Helena Galhardas, Julien Leblay, Minh-Huong Le Nguyen, et al.. ConnectionLens: Finding Connections Across Heterogeneous Data Sources. Proceedings of the VLDB Endowment (PVLDB), VLDB Endowment, 2018, 11, pp.4. ⟨10.14778/3229863.3236252⟩. ⟨hal-01841009⟩

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