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GraphQueryML: Using Machine Learning to Optimize Queries in Graph Databases

English title GraphQueryML: Using Machine Learning to Optimize Queries in Graph Databases
Applicant Stockinger Kurt
Number 192105
Funding scheme Project funding (Div. I-III)
Research institution ZHAW Zürcher Hochschule für Angewandte Wissenschaften
Institution of higher education Zurich University of Applied Sciences - ZHAW
Main discipline Information Technology
Start/End 01.07.2021 - 30.06.2024
Approved amount 359'527.00
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Keywords (4)

Machine learning; Query optimization; Graph databases; Relational databases

Lay Summary (German)

Lead
Mit maschinellem Lernen das "Gehirn" von Datenbanken verbessern
Lay summary

Die Abfrageoptimierung (Query Optimization) ist eines der schwierigsten Probleme der Datenbankforschung. Ein Abfrageoptimierer kann als das "Gehirn" des Systems betrachtet werden, das dafür sorgt, dass Abfragen effizient ausgeführt werden. Auch nach mehreren Jahrzehnten der Forschung sind viele Teilprobleme der Abfrageoptimierung noch ungelöst. Das Ziel dieses Projekts ist es, mit Hilfe von maschinellem Lernen das "Gehirn" von relationalen Datenbanksystemen sowie von Graphdatenbanksystemen zu verbessern.

Direct link to Lay Summary Last update: 09.04.2021

Responsible applicant and co-applicants

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Associated projects

Number Title Start Funding scheme
167149 Bio-SODA: Enabling Complex, Semantic Queries to Bioinformatics Databases through Intuitive Searching over Data 01.04.2017 NRP 75 Big Data

Abstract

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.
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