Modern trends in data collection are bringing current mainstream techniques for database query processing to their limits. Consequently, various novel approaches for efficient query processing are being actively studied. One such approach is based on hypertree decompositions (HDs), which have been shown to carry great potential to process complex queries more efficiently and with stronger theoretical guarantees. However, using HDs for query execution relies on the difficult task of computing decompositions of the query structure, which guides the efficient execution of the query. From theoretical results we know that the performance of purely sequential methods is inherently limited, yet the problem is susceptible to parallelisation. In this paper we propose the first algorithm for computing hypertree decompositions that is well-suited for parallelisation. The proposed algorithm log-k-decomp requires only a logarithmic number of recursion levels and additionally allows for highly parallelised pruning of the search space by restriction to balanced separators. We provide detailed experimental evaluation over the HyperBench benchmark and demonstrate that our approach is highly effective especially for complex queries.

中文翻译：

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对数递归深度的快速并行超树分解

数据收集的现代趋势正在将当前用于数据库查询处理的主流技术发挥到极致。因此，正在积极地研究用于有效查询处理的各种新颖方法。一种这样的方法是基于超树分解（HD）的，它已被证明具有巨大的潜力，可以更有效地处理更复杂的查询，并且具有更强的理论保证。但是，将HD用于查询执行依赖于计算查询结构分解的艰巨任务，这指导了查询的有效执行。从理论结果可以知道，纯粹顺序方法的性能固有地受到限制，但是问题很容易并行化。在本文中，我们提出了第一种非常适合并行化的用于计算超树分解的算法。提出的算法log-k-decomp仅需要递归级数的对数，并且通过限制平衡分隔符，还允许对搜索空间进行高度并行的修剪。我们提供了有关HyperBench基准测试的详细实验评估，并证明了我们的方法非常有效，尤其是对于复杂查询。