Improving Recommending and Understanding Schemas and Their Provenance

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，这些数据往往非常难以理解和信任，即使是该领域的专家。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。 缺乏理解和信任会导致大大小小的问题。例如，最近的金融危机部分是由于无法跟踪多个来源的资金（例如，共同基金与储蓄账户在不同的数据库中）。由于无法理解和信任分散在各种来源的数据，监管机构直到崩盘后才意识到一些有问题的资金流动。在较小的范围内，如果无法找到评估建筑设计选择所需的所有数据，就不可能轻松探索建筑设计的替代方案，这会导致扼杀创新和效率较低的建筑。 我提出的研究将提高用户访问他们需要的数据的能力，并相信这些数据已经得到充分的管理，可以做他们需要做的事情。这将建立在我目前研究经济数据和土木工程案例的基础上，并将与我在推荐系统中学到的技术一起工作，以帮助向用户推荐数据项。在这个建议中，我描述了我打算与我的学生在该领域合作的一些具体方向，包括：（1）使用查询日志来帮助推荐用户可能感兴趣的数据部分;（2）帮助用户理解XML文件中出现的复杂连接;以及（3）提高对出处数据的理解。

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. The lack of understanding and trust can cause problems both large and small. For example, the recent financial crisis was partially caused by the inability to track money across multiple sources (e.g., mutual funds are in a different database than savings accounts). Because it was impossible to understand and trust the data that was spread across various sources, regulators did not realize some of the problematic flows of money until after the crash. On a smaller scale, without being able to find all of the data needed in order to evaluate building design choices, it is impossible to easily explore building design alternatives, which leads to stifled innovation and less--efficient buildings. My proposed research will improve the ability of users to access data that they need and trust that this data is well curated enough to do what they need it to do. This will build on my current work that has looked at case studies in economic data and civil engineering, and will work with the techniques that I have learned in working with recommender systems to help recommend data items to users. In this proposal I describe a number of specific directions that I intend to work on with my students in that space, including: (1) using query logs to help recommend parts of the data that users may be interested in; (2) helping users understand the complex connections that appear in XML files; and (3) improving the understanding of provenance data.