  The Rete forward inference algorithm forms the basis for many rule engines
deployed today, but it exhibits the following problems: (1) the caching of all
intermediate join results, (2) the processing of all rules regardless of the
necessity to do so (stemming from the underlying forward inference approach),
(3) not defining the join order of rules and its conditions, significantly
affecting the final run-time performance, and finally (4) pointer chasing due
to the overall network structure, leading to inefficient usage of the CPU
caches caused by random access patterns. The Hiperfact approach aims to
overcome these shortcomings by (1) choosing cache efficient data structures on
the primary rank 1 fact index storage and intermediate join result storage
levels, (2) introducing island fact processing for determining the join order
by ensuring minimal intermediate join result construction, and (3) introducing
derivation trees to allow for parallel read/write access and lazy rule
evaluation. The experimental evaluations show that the Hiperfact prototype
engine implementing the approach achieves significant improvements in respect
to both inference and query performance. Moreover, the proposed Hiperfact
engine is compared to existing engines in the context of a comprehensive
benchmark.
