一种基于缓存的增强型知识库查询优化器
A Cache-based Optimizer for Querying Enhanced Knowledge Bases
摘要：
随着物联网（IoT）等新兴技术的出现，我们可以以更高的粒度收集物理世界和环境的信息，这些详细的知识将在提高生产力，运营效率，决策以及确定新的经济增长商业模式方面发挥关键作用。由于到知识库的接口的连接的有限能力和高延迟，有效地发现和查询这样的知识仍然是一个关键挑战，SPARQL端点。在这篇文章中，我们提出了一个查询系统SPARQL端点的知识库，执行查询速度比国家的最先进的系统。我们的系统采用了基于缓存的优化方案，通过预取和缓存预测的潜在查询结果来提高查询性能。通过对DBpedia和Linked GeoData的SPARQL端点的查询集的评价，验证了该方法的有效性。
英文摘要：
With recent emerging technologies such as the Internet of Things (IoT), information collection on our physical world and environment can be achieved at a much higher granularity and such detailed knowledge will play a critical role in improving the productivity, operational effectiveness, decision making, and in identifying new business models for economic growth. Efficient discovery and querying such knowledge remains a key challenge due to the limited capability and high latency of connections to the interfaces of knowledge bases, e.g., the SPARQL endpoints. In this article, we present a querying system on SPARQL endpoints for knowledge bases that performs queries faster than the state-of-the-art systems. Our system features a cache-based optimization scheme to improve querying performance by prefetching and caching the results of predicted potential queries. The evaluations on query sets from SPARQL endpoints of DBpedia and Linked GeoData showcase the effectiveness of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
