Distributed Keyword Search over Graph Databases using IBM Analytics Platform

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