Section: Research Program

Massively Distributed Data Management Systems

Large and increasing data volumes have raised the need for distributed storage architectures. Among such architectures, computing in the cloud is an emerging paradigm massively adopted in many applications for the scalability, fault-tolerance and elasticity features it offers, which also allows for effortless deployment of distributed and parallel architectures. At the same time, interest in massively parallel processing has been renewed by the MapReduce model and many follow-up works, which aim at simplifying the deployment of massively parallel data management tasks in a cloud environment. For these reasons, cloud-based stores are an interesting avenue to explore for handling very large volumes of RDF data.

Our research aims at taking advantage of such widely available, large-scale distributed architectures to build scalable platforms for massively distributed management of complex data. We consider many different wide-scale distributed back-ends in this context, ranging from those provided by commercial cloud platforms to simple MapReduce and to more complex extensions thereof. Beyond these architectures that are characterized by a single master node (a single point of control and distribution), we also explored ad-hoc, peer-to-peer style data management, which is more suitable in certain contexts, in particular for disseminating high-velocity data based on the similarity of interests among peers.

This line of research is part of our participation to the Datalyse project previously mentioned, as well as the KIC EIT ICT Labs Europa activity, now in its third year, part of the “Computing in the Cloud” action line.

A recent development in this area is the start of our collaboration with social scientists from Univ. Paris-Sud , working on the management of innovation; we have started two collaborative research projects (ANR “Cloud-Based Organizational Design” and PEPS “Business Models for the Cloud”) where we seek to build an interdisciplinary approach (both from a computing and from a business management perspective) on the adoption of cloud technologies within an enterprise.