Stardog is a commercial knowledge graph platform at the heart of which lies a graph database. It manages graph data in the form of RDF (Resource Data Framework) triples and natively implements SPARQL 1.1 graph query language. This talk will present the general architecture of the query engine and then will delve deep into the internals of the query optimiser, particularly, graph statistics and cardinality estimations for graph patterns.

Differently from some early SPARQL systems Stardog is not built on top of a relational database. Nonetheless the talk will highlight how it takes advantage of many foundational aspects of relational query optimisation, such as rewriting algebraic expressions, cost-based optimisation, planning joins, etc. At the same time some aspects, such as the lack of a rigid schema, like foreign key constraints or column data types, present unique challenges for the query optimiser.

This talk is part of the Vaccination Database (Booster) Tech Talk Seminar Series.

Bio:
Pavel Klinov has led the query engine team at Stardog since 2011 (with a short academic break in 2012-2015 to work on an automated reasoning project at the University of Ulm). Pavel has overseen Stardog's query engine evolve from a very simple heuristic optimiser in 2011 to a sophisticated cost-based optimiser in 2022 where most inputs for the cost model come from cardinality estimations. His team is responsible for both query performance optimisation work and implementing new features for the query language, such as recursive path queries. Prior to joining Stardog Pavel earned his PhD on performance of probabilistic logic algorithms at the University of Manchester, UK.

More Info: https://db.cs.cmu.edu/seminar2022-booster#db8