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As part of IRIS (Integrated Rule Inference System) project , we have developed different optimization techniques for efficient reasoning. IRIS is a reasoner based on a well-studied rule-based knowledge representation called Datalog. IRIS implements a couple of evaluation strategies (e.g., naïve and semi-naïve method, Query Sub-Query) as well as optimization techniques (e.g., Magic Sets).
We are continuously improving the core engine. In order to enable better evaluation of different optimization this thesis shall design and implement a benchmark framework, which allows to run different queries against various datalog programs. The framework shall measure the registration and evaluation times and allow to compare them for further analysis.
The aim of this thesis is to develop and implement a benchmark which will be used for comparing the performance of different optimization techniques that are being developed for IRIS. The scope of the thesis can also be adopted for a master theis.