面向大开放关联数据的工作负载感知视图实体化

Tomasz Zlamaniec, K. Chao, Nick Godwin
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引用次数: 0

摘要

公共组织将其大型数据集数字化并作为开放链接数据发布给公众用户查询和其他应用程序以供进一步利用是一种趋势。随着时间的推移,不同用户的不同频率的查询会给服务器带来不同的工作负载模式,从而无法保证高峰使用时的QoS。物化是一种众所周知的降低峰值的有效方法,但由于图式的频繁演变,语义网并未采用物化方法。这项研究能够基于以前的查询估计工作负载,分析和规范化它们的结构以实现视图,并将查询映射到具有填充数据的视图。通过分析各个视图的访问模式对整个系统工作负载的影响,所提出的模型旨在选择能够最大限度地减少峰值工作负载的候选视图。因此,使用新选择方法的系统可以在最需要的时候提供更高的查询吞吐量,而不是平等地优化所有查询,从而允许更多的并发用户,而不会在高峰使用期间损害QoS。最后,通过两个案例对所提出的方法进行了评价。
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Workload-Aware Views Materialization for Big Open Linked Data
It is a trend for the public organizations to digitalize and publish their large dataset as open linked data to the public users for queries and other applications for further utilizations. Different users’ queries with various frequencies over time create different workload patterns to the servers which cannot guarantee the QoS during peak usages. Materialization is a well-known effective method to reduce peaks, but it is not used by semantic webs, due to frequently evolving schema. This research is able to estimate workloads based on previous queries, analyze and normalize their structures to materialize views, and map the queries to the views with populated data. By analyzing how access patterns of individual views contribute to the overall system workload, the proposed model aims at selection of candidates offering the highest reduction of the peak workload. Consequently, rather than optimizing all queries equally, a system using the new selection method can offer higher query throughput when it is the most needed, allowing for a higher number of concurrent users without compromising QoS during the peak usage. Finally, two case studies were used to evaluate the proposed method.
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