论文标题
答案图：分解在大图中很重要
Answer Graph: Factorization Matters in Large Graphs
论文作者
论文摘要
我们评估SPARQL结合查询（CQS）的答案图方法是首先找到一个分解的答案集，一个答案图，然后从中找到嵌入的单元。这种方法可以大大降低评估CQ的成本。这提供了第二个优势：我们可以构建基于成本的计划者。我们介绍了答案方法，并概述了我们的原型系统，线框。然后，我们通过Yago2S数据集的微基准提供概念证明，并具有两个普遍的查询形状，雪花和钻石。我们将线框对这些的性能与PostgreSQL，Virtuoso，MonetDB和Neo4J进行了比较，以说明我们的答案方法的性能优势。
Our answer-graph method to evaluate SPARQL conjunctive queries (CQs) finds a factorized answer set first, an answer graph, and then finds the embedding tuples from this. This approach can reduce greatly the cost to evaluate CQs. This affords a second advantage: we can construct a cost-based planner. We present the answer-graph approach, and overview our prototype system, Wireframe. We then offer proof of concept via a micro-benchmark over the YAGO2s dataset with two prevalent shapes of queries, snowflake and diamond. We compare Wireframe's performance over these against PostgreSQL, Virtuoso, MonetDB, and Neo4J to illustrate the performance advantages of our answer-graph approach.
