Linked Data is a standardized way of publishing interoperable data to the Web, with the goal of making it easier for intelligent applications to access, combine and interpret data from heterogeneous sources. Typically, Linked Data applications either expose or consume data in the form of triples specified in RDF and stored in a graph database, commonly referred to as a triplestore, such as Virtuoso, Fuseki, GraphDB, etc. However, directly working with these triplestores is notoriously difficult for application developers, because of the lack of developer-friendly tools and frameworks that are available. For this reason, redpencil created the open source semantic.works microservice framework, which provides many components that are necessary for a typical single-page web application out-of-the-box. Think of a frontend framework, an authorization component, a search index, etc.
While semantic.works supports a variety of triplestore implementations in theory, it has only been battle-tested in production environments using a single technology: Virtuoso. In this thesis, we want to implement the means to use other triplestores, such as GraphDB, and evaluate the impact of the choice of underlying database technology on the overall application. The student will implement the coupling of these triplestores to the existing microservice architecture, and set up multiple test scenarios to evaluate the performance in terms of speed, reliability, and computing resources. Semantic.works is already in use in several production environments, so the conclusions and recommendations of this thesis could have a direct and significant impact on real-world, in-use applications.
Company: redpencil.io
Contacts:
- Academic supervisor: Prof. Christophe Debruyne (ULiège)
- Industry supervisor: Dr. Tom De Nies (< email slettet af sikkerhedsmæssige årsager >)
- Administrative contact: Johan Delauré (< email slettet af sikkerhedsmæssige årsager >)
This topic is an applied research topic, and students will be encouraged to be involved in the potential dissemination of the results.