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当伸手云：使用并行硬件的链接发现
When to Reach for the Cloud: Using Parallel Hardware for Link Discovery |
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| 课程网址: |
http://videolectures.net/eswc2013_ngonga_ngomo_cloud/
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| 主讲教师: |
Axel Polleres, Axel-Cyrille Ngonga Ngomo
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| 开课单位: |
维也纳经济与商业大学
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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: |
随着 web 上可用的 rdf 数据量不断增加, 在知识库中发现数据集之间的链接和资源重复数据删除已成为至关重要的任务。在过去几年中, 开发了几种链接发现方法来解决链接发现所固有的运行时和复杂性问题。然而, 到目前为止, 执行链路发现任务的硬件资源管理却很少受到关注。本文通过研究系统资源在链路发现中的有效利用, 解决了这一研究差距。我们针对三种不同的并行处理模式实现了 hr3 方法, 包括使用 gpu 和 mapreduce 平台。我们还对这些实现执行全面的性能比较。我们的结果表明, 某些似乎需要云计算技术的任务实际上可以使用标准并行硬件来完成。此外, 我们的评估提供了盈亏平衡点, 可作为决定何时使用哪种硬件进行链接发现的指导。
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| 课程简介: |
With the ever-growing amount of RDF data available across the Web, the discovery of links between datasets and deduplication of resources within knowledge bases have become tasks of crucial importance. Over the last years, several link discovery approaches have been developed to tackle the runtime and complexity problems that are intrinsic to link discovery. Yet, so far, little attention has been paid to the management of hardware resources for the execution of link discovery tasks. This paper addresses this research gap by investigating the efficient use of hardware resources for link discovery. We implement the HR3 approach for three different parallel processing paradigms including the use of GPUs and MapReduce platforms. We also perform a thorough performance comparison for these implementations. Our results show that certain tasks that appear to require cloud computing techniques can actually be accomplished using standard parallel hardware. Moreover, our evaluation provides break-even points that can serve as guidelines for deciding on when to use which hardware for link discovery.
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| 关 键 词: |
计算机科学; 大数据; 语义网
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-13：邬启凡(课程编辑志愿者)
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| 阅读次数: |
41
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