CEDAR Research team
Our research aims at models, algorithms and tools for highly efficient, easy-to-use data and knowledge management; throughout our research, performance at scale is a core concern, which we address, among other techniques, by designing algorithms for a cloud (massively parallel) setting. Our scientific contributions fall in three interconnected areas:Expressive models for new applications
As data and knowledge applications keep extending to novel application areas, we work to devise appropriate data and knowledge models, endowed with formal semantics, to capture such applications' needs. This work mostly concerns the domains of data journalism and journalistic fact checking;Optimization and performance at scale
This topic is at the heart of Y. Diao's ERC project “Big and Fast Data”, which aims at optimization with performance guarantees for real-time data processing in the cloud. Machine learning techniques and multi-objectives optimization are leveraged to build performance models for data analytics the cloud. The same boal is shared by our work on efficient evaluation of queries in dynamic knowledge bases.Data discovery and exploration
Today's Big Data is complex; understanding and exploiting it is difficult. To help users, we explore: compact summaries of knowledge bases to abstrac their structure and help users formulate queries; interactive exploration of large relational databases; techniques for automatically discovering interesting information in knowledge bases; and keyword search techniques over Big Data sources.