Hash-processing of universal quantification-like queries dealing with requirements and prohibitions

This paper is focused on flexible universal quantification-like queries handling simultaneously positive and negative preferences (requirements or prohibitions). We emphasise the performance improvement of the considered operator by proposing new variants of the classical hash-division algorithm. The issue of answers ranking is also dealt with. We target in our work the in memory databases systems (also called main-memory database systems) with a very large volume of data. In these systems, all the data is primarily stored in the RAM of a computer. We have introduced a parallel implementation of the operator that takes into account the data skew issue. Our empirical analysis for both sequential and parallel versions shows the relevance of our approach. They demonstrate that the new processing of the mixed operator in a main-memory database achieves better performance compared to the conventional ones, and becomes faster through parallelism.