PRESTO：基于子图重叠的RDF概率基数估计
PRESTO: Probabilistic Cardinality Estimation for RDF Queries Based on Subgraph Overlapping
摘要：
在查询优化中，准确的基数估计对于寻找最优查询计划至关重要。由于缺少明确的模式和RDF查询中过多的连接，这对RDF来说尤其具有挑战性。现有的方法通常基于三元组的计数来收集统计信息，并将查询的基数估计为其连接组件的乘积，即使每个组件的估计是准确的，错误也会累积。相对于现有的方法，我们提出PRESTO，基数估计方法，是基于子图的计数，而不是三元组，并使用概率的方法来估计基数的RDF查询作为一个整体。PRESTO避免了现有方法的一些主要问题，并且能够在有限内存约束下准确地估计任意查询。我们评估PRESTO与YAGO和PRESTO是更准确的简单和复杂的查询。
英文摘要：
In query optimisation accurate cardinality estimation is essential for finding optimal query plans. It is especially challenging for RDF due to the lack of explicit schema and the excessive occurrence of joins in RDF queries. Existing approaches typically collect statistics based on the counts of triples and estimate the cardinality of a query as the product of its join components, where errors can accumulate even when the estimation of each component is accurate. As opposed to existing methods, we propose PRESTO, a cardinality estimation method that is based on the counts of subgraphs instead of triples and uses a probabilistic method to estimate cardinalities of RDF queries as a whole. PRESTO avoids some major issues of existing approaches and is able to accurately estimate arbitrary queries under a bound memory constraint. We evaluate PRESTO with YAGO and show that PRESTO is more accurate for both simple and complex queries.
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