Integrating heterogeneous content has become a key hurdle in the deployment of Big Data applications, due to the meteoric rise of 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.
GraphInt proposes an ambitious overhaul of information integration techniques embracing the scale and heterogeneity of today’s data. We propose the use of expressive and heterogeneous graphs of entities to continuously and dynamically interrelate disparate pieces of content while capturing their idiosyncrasies. Our project focuses on three core issues related to extremely large and heterogeneous information graphs:
This project is supported by a generous grant from the ERC and will run from 2016 to 2020.