IMEC - IDLAB : Solid - a post-Big Data perspective by Ruben Verborgh

We’re living in a data-driven economy, and that won’t change anytime soon. Companies, start-ups, organisations, and governments all require some of our data to provide us with the services we want and need. Unfortunately, decades of Big Data thinking has led many companies to a consequential fallacy: the belief that they need to harvest and maintain that personal data themselves in order to deliver their services and thus survive in the data-driven economy. This prompted a never-ending rat race, dominated by a handful of large players and driven by a deeply flawed notion of “winning”. Data greed has falsified competition and stifled innovation from the moment data collection became more important than quality of experience. A way out of this dead end is to put people fully in control of their own data by equipping them with a personal data vault. Doing so creates a level playing field where more data is available to put to good use. In this talk, I will explain the Solid ecosystem of personal data vaults, and discuss our plans within Flanders.

About Prof. Ruben Verborgh

Ruben Verborgh is a professor of Decentralized Web technology at IDLab, Ghent University – imec, and a research affiliate at the Decentralized Information Group of CSAIL at MIT. Additionally, he acts as a technology advocate for Inrupt and the Solid ecosystem wherein people and organizations control their own data. He aims to build a more intelligent generation of clients for a decentralized Web at the intersection of Linked Data and hypermedia-driven Web APIs. Through the creation of Linked Data Fragments, he introduced a new paradigm for query processing at Web scale. He has co-authored two books on Linked Data, and contributed to more than 250 publications for international conferences and journals on Web-related topics.

About Bart Bogaerts

Bart Bogaerts is an assistant professor at the AI Lab of the Vrije Universiteit Brussel.

His research interests are spread throughout the field of knowledge representation. They range from high-level representation languages to efficient SAT solving, from abstract, algebraical frameworks to unify semantics of logics to implementation of knowledge base systems, from applications of KR to integration of declarative problem solving paradigms, …

Recently some of his main focus points have been 1) Explaining inferences made by (constraint) solver, from a perspective of human-understandability, as well as verification of the result, 2) Unifying frameworks for knowledge representation (approximation fixpoint theory, justification theory), and 3) reasoning with distributed knowledge.