Big Data Curation

大数据策展

• 批准号: 217606-2013
• 负责人: Miller, Renée
• 金额: $ 3.21万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=594017
• 关键词: Big; Data; Curation

The wide-spread availability of vast amounts of structured data has made it possible to adopt algorithmic and statistical methods to solve data integration problems in new ways. A 2009 report indicated there were already over 100 million structured data tables on the Web. Efforts like Google's Fusion Tables make it easy to publish and share structured and semi-structured data. However, we are far from being able to automatically integrate and align this data so that we can easily use these rich data sources collectively in query answering, decision making, and prediction. Semantic integration at web-scale is a "grand challenge" problem. There is emerging evidence that some of the nuanced semantic differences that eluded enterprise-scale integration solutions, can now be discovered and resolved using knowledge from combinations of web data sources, some which have been curated by incentivized user communities who value the creation of reliable data. The deep heterogeneity and dynamic nature of this data demands new methods for not only alignment, but also for tracing the provenance of alignment decisions, and for maintaining and publishing consistent, properly aligned data. In this work, we will develop new methods for big data curation. Data curation involves the preparation of data for re-use or publishing, the proper modeling of context and semantics, and more generally the care of data to make it of maximal value to users. For big data, we must automate curation tasks to the extent possible and recognize the value of reusing data curated by user communities (such as open ontologies) to enhance and guide automation when publishing new data sources.

大量结构化数据的广泛可用性使得采用算法和统计方法以新的方式解决数据集成问题成为可能。2009年的一份报告显示，Web上已经有超过1亿个结构化数据表。像Google的Fusion Tables这样的努力可以轻松发布和共享结构化和半结构化数据。然而，我们还远远不能自动集成和调整这些数据，以便我们可以轻松地在查询回答，决策和预测中共同使用这些丰富的数据源。网络规模的语义集成是一个“大挑战”问题。有新的证据表明，一些微妙的语义差异，逃避企业规模的集成解决方案，现在可以发现和解决使用的知识，从组合的网络数据源，其中一些已策划的激励用户社区谁的价值创造可靠的数据。这种数据的深度异质性和动态性要求新的方法不仅用于对齐，而且用于跟踪对齐决策的起源，以及用于维护和发布一致的，正确对齐的数据。在这项工作中，我们将开发大数据策展的新方法。数据策展涉及准备数据以供重用或发布，对上下文和语义进行适当的建模，以及更一般地对数据进行护理，使其对用户具有最大价值。对于大数据，我们必须尽可能地自动化管理任务，并认识到重用用户社区管理的数据（例如开放本体）的价值，以增强和指导发布新数据源时的自动化。