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Graph Databases

Research on foundational, algorithmic, and systems aspects of graph databases.

The last fifteen years have seen an unprecedented accumulation of data by applications going well beyond traditional databases. These days we are witnessing the ever increasing demand for a new data format: graph-structured data. The need for processing graph data is particularly important in applications such as social networks and Semantic Web, where the underlying data are naturally modelled as a graph. Other applications of graph data appear in biology, cheminformatics, knowledge discovery, network traffic, computer vision, intelligence analysis and crime detection.

We have several projects related to the study of graph databases. Our objective is to provide foundations, practical techniques and a toolbox for querying graph data, independent of individual applications and readily applicable in all of the main tasks for querying graph data. Specifically we concentrate on the design and analysis of query languages for graph data, on algorithms for efficient query evaluation over large and/or distributed graphs, and on designing and building tools for providing approximate query answering over graphs. We are looking for PhD students with strong background in both theoretical CS (for the foundational tasks) and systems aspects (for the design and evaluation of query processing techniques).

Potential supervisors

Wenfei Fan

Leonid Libkin

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