  Variability-aware computing is the efficient application of programs to
different sets of inputs that exhibit some variability. One example is program
analyses applied to Software Product Lines (SPLs). In this paper we present the
design and development of a variability-aware version of the Souffl\'{e}
Datalog engine. The engine can take facts annotated with Presence Conditions
(PCs) as input, and compute the PCs of its inferred facts, eliminating facts
that do not exist in any valid configuration. We evaluate our variability-aware
Souffl\'{e} implementation on several fact sets annotated with PCs to measure
the associated overhead in terms of processing time and database size.
