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To efficiently answer queries, datalog systems often materialise allconsequences of a datalog program, so the materialisation must be updatedwhenever the input facts change. Several solutions to the materialisationupdate problem have been proposed. The Delete/Rederive (DRed) and theBackward/Forward (B/F) algorithms solve this problem for general datalog, butboth contain steps that evaluate rules 'backwards' by matching their heads to afact and evaluating the partially instantiated rule bodies as queries. We showthat this can be a considerable source of overhead even on very small updates.In contrast, the Counting algorithm does not evaluate the rules 'backwards',but it can handle only nonrecursive rules. We present two hybrid approachesthat combine DRed and B/F with Counting so as to reduce or even eliminate'backward' rule evaluation while still handling arbitrary datalog programs. Weshow empirically that our hybrid algorithms are usually significantly fasterthan existing approaches, sometimes by orders of magnitude.

Optimised Maintenance of Datalog Materialisations