Improving Recommending and Understanding Schemas and Their Provenance

改进推荐和理解模式及其来源

• 批准号: RGPIN-2017-04112
• 负责人: Pottinger, Rachel
• 金额: $ 3.06万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634773
• 关键词: Improving; Recommending; Understanding; Schemas; Their

We are drowning in a sea of data. Increasingly, jobs that used to rely on other skills now rely on the ability to interpret, understand, and trust data. For example, many biologists now spend less time in the lab and more time trying to analyze the data that they have created. Increasingly, thanks to improved tools for creating data, they are expected to manage and understand this data by themselves. Unfortunately, this data is often exceedingly difficult to understand and trust, even for experts in the field. There are many reasons for this. One reason is that the representation of the data is often designed for computers, not for people (i.e., storage, not accessibility). Another reason for this is that users may spend a lot of time using systems where data is combined from multiple sources, leaving them stuck having trouble integrating data that they neither understand nor trust.

我们淹没在数据的海洋中。过去依赖其他技能的工作现在越来越依赖于解释、理解和信任数据的能力。例如，许多生物学家现在花在实验室的时间更少，而花更多的时间试图分析他们创建的数据。由于创建数据的工具不断改进，人们越来越希望他们自己管理和理解这些数据。不幸的是，这些数据通常非常难以理解和信任，即使是该领域的专家也是如此。原因有很多。一个原因是数据的表示通常是为计算机设计的，而不是为人设计的（即，存储，而不是可访问性）。另一个原因是，用户可能会花费大量时间使用来自多个来源的数据组合的系统，这使得他们难以集成他们既不理解也不信任的数据。