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没有适合所有的大小 - 使用SPARQL和RDF聚合视图运行星型模式基准
No Size Fits All - Running the Star Schema Benchmark with SPARQL and RDF Aggregate Views |
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| 课程网址: |
http://videolectures.net/eswc2013_kaempgen_views/
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| 主讲教师: |
Benedikt Kämpgen, Axel Polleres
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| 开课单位: |
卡尔斯鲁厄理工学院
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| 开课时间: |
2013-07-08
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| 课程语种: |
英语
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| 中文简介: |
作为关联数据发布的统计信息承诺将有效的提取，转换和加载（ETL）转换为数据库以供决策支持。在行业中实现分析查询功能的主要方式是使用星型模式（ROLAP）将OLAP查询转换为关系数据库上的SQL查询的专用引擎。比ROLAP更直接的方法是将统计链接数据加载到RDF存储中并使用SPARQL回答OLAP查询。但是，我们假设通用三重存储 - 就像典型的关系数据库一样 - 不适合分析工作负载，需要通过OLAP到SPARQL引擎进行补充。为了给出这种引擎需求的经验论证，我们首先比较生成的SPARQL和ROLAP SQL查询的性能。其次，我们测量RDF聚合视图的性能增益，类似于ROLAP中的聚合表，实现了数据立方体的各个部分。
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| 课程简介: |
Statistics published as Linked Data promise efficient extraction, transformation and loading (ETL) into a database for decision support. The predominant way to implement analytical query capabilities in industry are specialised engines that translate OLAP queries to SQL queries on a relational database using a star schema (ROLAP). A more direct approach than ROLAP is to load Statistical Linked Data into an RDF store and to answer OLAP queries using SPARQL. However, we assume that general-purpose triple stores – just as typical relational databases – are no perfect fit for analytical workloads and need to be complemented by OLAP-to-SPARQL engines. To give an empirical argument for the need of such an engine, we first compare the performance of our generated SPARQL and of ROLAP SQL queries. Second, we measure the performance gain of RDF aggregate views that, similar to aggregate tables in ROLAP, materialise parts of the data cube.
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| 关 键 词: |
关联数据; 星型模式; SQL查询
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-04-14：lxf
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| 阅读次数: |
27
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