The main drawbacks of current reasoning methods over ontologies are they struggle to provide scalability for large datasets. The batch processing reasoners who provide the best scalability so far are unable to infer knowledge from evolving data. We contribute to solving these problems by introducing Slider, an efficient incremental reasoner. Slider exhibits a performance improvement by more than a 70% compared to the OWLIM-SE reasoner. Slider is conceived to handle expanding data from streams with a growing background knowledge base. It natively supports df and RDFS, and its architecture allows to extend it to more complex fragments with a minimal effort.