Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

Much of the world's high-quality enterprise and social data are stored as semi-structured and structured data.x000D
This includes enterprises' RDBMSs, knowledge graphs, and social networks. All these data collections eitherx000D
are defined already as graph databases or can be re-modeled as graphs. Over the past decade, we havex000D
witnessed advances in storing and manipulating structured and semi-structured data, but we have not seenx000D
much progress in search over them.x000D
As Surajit Chaudhuri (a distinguished scientist at Microsoft Research) addressed in his keynote talk at IEEEx000D
Data Engineering Conference in 2015, search over structured databases has fallen behind search overx000D
unstructured data. While scientists and business users look for exciting, actionable discoveries from theirx000D
heterogeneous datasets, the need to provide effective search is profound. If we cannot deliver a powerful searchx000D
system, much of the big data dream will not be achieved.x000D
The existence of structure in graph databases offers potent means for data exploration compared with a set ofx000D
disparate, unstructured documents. Traditionally, to access structured databases, users have to learn structuredx000D
query languages, such as SQL or SPARQL. They also need to learn the schemas of the databases in which theyx000D
have an interest. However, a non-technical user (i.e., anyone who does not know query languages or is notx000D
familiar with the given schema) is effectively locked out.x000D
Exploring graph databases for non-technical users is currently not supported in IBM's Analytics Platform. Thisx000D
project will provide a solution for this problem. More specifically, we focus on designing a distributed andx000D
parallel keyword search system using IBM Analytics Platform to empower non-technical users to explore graphx000D
databases.