Hypertree decompositions provide a way to evaluate Conjunctive Queries (CQs) in polynomial time, where the exponent of this polynomial is determined by the width of the decomposition. In theory, the goal of efficient CQ evaluation therefore has to be a minimisation of the width. However, in practical settings, it turns out that there are also other properties of a decomposition that influence the performance of query evaluation. It is therefore of interest to restrict the computation of decompositions by constraints and to guide this computation by preferences. To this end, we propose a novel framework based on candidate tree decompositions, which allows us to introduce soft hypertree width (shw). This width measure is a relaxation of hypertree width (hw); it is never greater than hw and, in some cases, shw may actually be lower than hw. ost importantly, shw preserves the tractability of deciding if a given CQ is below some fixed bound, while offering more algorithmic flexibility. In particular, it provides a natural way to incorporate preferences A prototype implementation and preliminary experiments confirm that this novel framework can indeed have a practical impact on query evaluation.

翻译：超树分解为合取查询（CQ）的多项式时间求值提供了一种方法，其中该多项式的指数由分解的宽度决定。因此，理论上高效CQ求值的目标必然是宽度最小化。然而，在实际场景中，分解的其他特性也会影响查询求值的性能。因此，通过约束限制分解的计算，并通过偏好引导这一计算，具有重要意义。为此，我们提出了一种基于候选树分解的新框架，该框架允许我们引入软超树宽度（shw）。这一宽度度量是超树宽度（hw）的松弛；它从不大于hw，并且在某些情况下，shw实际上可能低于hw。最重要的是，shw保留了判定给定CQ是否低于某个固定界限的可处理性，同时提供了更多的算法灵活性。特别是，它提供了一种自然的方式来整合偏好。原型实现和初步实验证实，这一新框架确实能对查询求值产生实际影响。