  The evaluation of Datalog rules over large Knowledge Graphs (KGs) is
essential for many applications. In this paper, we present a new method of
materializing Datalog inferences, which combines a column-based memory layout
with novel optimization methods that avoid redundant inferences at runtime. The
pro-active caching of certain subqueries further increases efficiency. Our
empirical evaluation shows that this approach can often match or even surpass
the performance of state-of-the-art systems, especially under restricted
resources.
