Improving Recommending and Understanding Schemas and Their Provenance

我们正在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，许多生物学家现在在实验室里花的时间更少了，而花了更多的时间试图分析他们创造的数据。越来越多地，由于数据创建工具的改进，他们被期望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，即使对该领域的专家来说也是如此。造成这种情况的原因有很多。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，导致他们在集成既不理解也不信任的数据时陷入困境。 缺乏理解和信任可能会造成大大小小的问题。例如，最近的金融危机的部分原因是无法追踪多个来源的资金(例如，共同基金与储蓄账户在不同的数据库中)。由于不可能理解和信任各种来源的数据，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，如果不能找到评估建筑设计选择所需的所有数据，就不可能轻松地探索建筑设计替代方案，这会导致创新受阻，建筑效率较低。 我提议的研究将提高用户访问他们需要的数据的能力，并相信这些数据得到了足够好的管理，可以做他们需要做的事情。这将建立在我目前的工作基础上，这些工作着眼于经济数据和土木工程方面的案例研究，并将使用我在使用推荐系统时学到的技术来帮助向用户推荐数据项。在这份提案中，我描述了我打算在该领域与我的学生合作的一些具体方向，包括：(1)使用查询日志帮助推荐用户可能感兴趣的部分数据；(2)帮助用户理解出现在XML文件中的复杂联系；以及(3)提高对来源数据的理解。

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.