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在内存预算下回答RDF数据立方体的来源感知查询

Answering Provenance-Aware Queries on RDF Data Cubes under Memory Budgets
课程网址: http://videolectures.net/iswc2018_razniewski_answering_provenance...  
主讲教师: Simon Razniewski
开课单位: 马克斯·普朗克信息学研究所
开课时间: 2018-11-22
课程语种: 英语
中文简介:
语义数据在Web上的日益普及以及SPARQL 1.1中对聚合查询的支持,促使人们对在线分析处理(OLAP)和RDF中的数据立方体产生了兴趣。在这种设置中的查询处理是具有挑战性的,因为SPARQL OLAP查询通常包含许多具有分组和聚合的三重模式。此外,在Web数据上回答查询的一个重要因素是其来源,即关于其来源的元数据。数据分析和访问控制中的一些应用程序需要使用来源元数据来扩充数据,并运行对该来源施加约束的查询。此任务称为出处感知查询应答。在这篇文章中,我们研究了在回答支持源的SPARQL查询时,缓存RDF立方体的某些部分并添加源信息的好处。我们提出了源位置感知缓存(PAC),一种基于RDF图的源位置感知分区的缓存方法,以及一种用于RDF立方体和具有聚合的SPARQL查询的有益模型。我们对真实数据和合成数据的结果表明,PAC在命中率和响应时间方面显著优于LRU策略(最近最少使用)和Jena TDB本地缓存。
课程简介: The steadily-growing popularity of semantic data on the Web and the support for aggregation queries in SPARQL 1.1 have propelled the interest in Online Analytical Processing (OLAP) and data cubes in RDF. Query processing in such settings is challenging because SPARQL OLAP queries usually contain many triple patterns with grouping and aggregation. Moreover, one important factor of query answering on Web data is its provenance, i.e., metadata about its origin. Some applications in data analytics and access control require to augment the data with provenance metadata and run queries that impose constraints on this provenance. This task is called provenance-aware query answering. In this paper, we investigate the benefit of caching some parts of an RDF cube augmented with provenance information when answering provenance-aware SPARQL queries. We propose provenance-aware caching (PAC), a caching approach based on a provenance-aware partitioning of RDF graphs, and a benefit model for RDF cubes and SPARQL queries with aggregation. Our results on real and synthetic data show that PAC outperforms significantly the LRU strategy (least recently used) and the Jena TDB native caching in terms of hit-rate and response time.
关 键 词: 在线分析处理; 分组和聚合的三重模式; 元数据来扩充数据; SPARQL查询的有益模型
课程来源: 视频讲座网
数据采集: 2023-01-16:cyh
最后编审: 2023-01-16:cyh
阅读次数: 27