Objective The present proposal tackles fundamental problems in data management, leveraging expressive, large-scale and heterogeneous graph structures in order to integrate both unstructured (e.g. text) and structured (e.g. relational) content. Integrating heterogeneous content has become a key hurdle in the deployment of Big Data applications, due to the meteoric rise of both machine and user-generated data storing information in a variety of formats. Traditional integration techniques cleaning up, fusing and then mapping heterogeneous data onto rigid abstractions fall short of accurately capturing the complexity and wild heterogeneity of today’s information. Having closely followed the emergence of heterogeneous information sources online, I am convinced that only an interdisciplinary approach drawing both from classical data management and from large-scale Web information processing techniques can solve the formidable data integration challenges that they pose. The following project proposes an ambitious overhaul of information integration techniques embracing the scale and heterogeneity of today’s data. I propose the use of expressive and heterogeneous graphs of entities to continuously and dynamically interrelate disparate pieces of content while capturing their idiosyncrasies. The following project focuses on three core issues related to large-scale and heterogeneous information graphs: i) the effective extraction of fined-grained information from unstructured sources and their proper integration into large-scale heterogeneous and probabilistic graphs, ii) the creation of novel physical storage structures and primitives to durably and efficiently manage the profusion of data considered by such graphs using clusters of commodity machines, and iii) the development of logical data abstraction mechanisms facilitating the effective and efficient resolution of complex analytic and data integration queries on top of the physical layer. Fields of science natural sciencescomputer and information sciencesdatabasesnatural sciencescomputer and information sciencesdata sciencebig datanatural sciencesmathematicspure mathematicsdiscrete mathematicsgraph theorynatural sciencesmathematicsapplied mathematicsstatistics and probabilitynatural sciencescomputer and information sciencesdata sciencedata processing Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Topic(s) ERC-CoG-2015 - ERC Consolidator Grant Call for proposal ERC-2015-CoG See other projects for this call Funding Scheme ERC-COG - Consolidator Grant Host institution UNIVERSITE DE FRIBOURG Net EU contribution € 1 998 339,00 Address AVENUE DE L EUROPE 20 1700 Fribourg Switzerland See on map Region Schweiz/Suisse/Svizzera Espace Mittelland Fribourg / Freiburg Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 1 998 339,00 Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all UNIVERSITE DE FRIBOURG Switzerland Net EU contribution € 1 998 339,00 Address AVENUE DE L EUROPE 20 1700 Fribourg See on map Region Schweiz/Suisse/Svizzera Espace Mittelland Fribourg / Freiburg Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 1 998 339,00