GraphQueryML: Using Machine Learning to Optimize Queries in Graph Databases

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Query optimization, i.e., the translation of a declarative query statement into an efficient query execution plan, is one of the central problems of database systems research. Even after four decades of research many sub-problems of query optimization are still unsolved. Acknowledging the fact that an increasing number of data sets is graph-structured and, in particular, represented in the Resource Description Framework (RDF) or in the Property Graph (PG) data model, this proposal explores the important open research problem of using machine learning for optimizing queries in graph databases. (1) We will design anddevelop a general query optimization framework that uses machine learning with focus on deep reinforcement learning. (2) We apply our framework to the optimization of SPARQL queries in RDF databases. (3) We will study the optimization of Cypher queries in property graph databases. Our approach has the great potential to enable novel discoveries both in the scientific community as well as in industry. In particular, the data-intensive bioinformatics community with the wide adoption of RDF databases will be benefit from accelerated queries across multiple RDF databases and thus enable shorter scientific discovery cycles.