基于HBase和MySQL集群的分布式语义Web数据管理
Distributed Semantic Web Data Management in HBase and MySQL Cluster
摘要：
网络上的各种计算和数据资源正在通过机器可解释的语义描述来增强，以促进更好的搜索、发现和集成。这种相互关联的元数据构成了语义Web，其数量可能会增加Web的规模。语义Web数据的有效管理（使用W3C的资源描述框架（RDF）表示）对于支持新的数据密集型、启用语义的应用程序至关重要。在这项工作中，我们研究和比较两种方法来分布式RDF数据管理基于新兴的云计算技术和传统的关系数据库聚类技术。特别是，我们设计了分布式RDF数据存储和查询方案HBase和MySQL集群和进行实证比较，这些方法的商品机器集群使用的数据集和查询从第三种来源的挑战和利哈伊大学基准。我们的研究揭示了有趣的模式，查询评估，表明我们的算法是有前途的，并表明云计算具有很大的潜力，可扩展的语义Web数据管理。
英文摘要：
Various computing and data resources on the Web are being enhanced with machine-interpretable semantic descriptions to facilitate better search, discovery and integration. This interconnected metadata constitutes the Semantic Web, whose volume can potentially grow the scale of the Web. Efficient management of Semantic Web data, expressed using the W3C's Resource Description Framework (RDF), is crucial for supporting new data-intensive, semantics-enabled applications. In this work, we study and compare two approaches to distributed RDF data management based on emerging cloud computing technologies and traditional relational database clustering technologies. In particular, we design distributed RDF data storage and querying schemes for HBase and MySQL Cluster and conduct an empirical comparison of these approaches on a cluster of commodity machines using datasets and queries from the Third Provenance Challenge and Lehigh University Benchmark. Our study reveals interesting patterns in query evaluation, shows that our algorithms are promising, and suggests that cloud computing has a great potential for scalable Semantic Web data management.
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