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We explore an approach to developing Datalog parallelization strategies that aims at good expected rather than worst-case performance. To illustrate, we consider a very simple parallelization strategy that applies to all Datalog programs. We prove that this has very good expected performance under equal distribution of inputs. This is done using an extension of O-l laws adapted to this context. The analysis is confirmed by experimental results on randomly generated data.doi:10.1016/s0304-3975(97)00091-1 fatcat:ib2qtagn4zfcdcorpx7z2okwb4