CRII: III: Managing Preference Data

偏好是归因于一群法官的一系列项目中的顺序。偏好数据以各种形式出现，例如排名列表和成对比较，并且在跨不同领域的大量应用程序中无处不在。在过去的十年里，偏好数据的数量、使用它的应用程序的多样性以及偏好数据分析方法的丰富性都急剧增加。应用的例子包括基因组数据分析中的排名聚合、选举中的选票管理以及电子商务中的推荐系统。该项目的目标是简化偏好数据的管理和分析。为了实现这一目标，PI和她的团队将开发一个名为DB4Pref的框架，为处理偏好数据的计算和数据科学家提供支持。作为该项目的一部分开发的模型、算法、数据和软件产品将公开提供。这项工作将对科学界产生影响，特别是对对生物信息学许多领域至关重要的功能基因组数据的分析，以及对社会应用的影响，在社会应用方面，它将使对用户偏好的高效率和有效性分析成为可能。PI将让研究生和本科生参与她的研究，并将继续与妇女和代表不足的少数族裔合作。这项研究的过程和结果都将被整合到PI教授的数据管理和数据科学课程中。这项工作将采用关系数据库模型，并将通过专门处理偏好数据的扩展来丰富它。具体地说，PI和她的团队将引入一种为偏好数据设计的特殊类型的关系，并将针对可以嵌入到SQL语句中的偏好关系提出可组合的运算符，以便在应用程序之间方便地重复使用。将开发偏好关系的可扩展实现，以及偏好聚类和排名聚合等分析。将偏好和偏好分析视为关系数据库中的一等公民，并通过(增强的)SQL查询使其可用，将带来两个重要的优势。第一个是可用性：因为SQL查询是以声明方式指定的，所以用户不必担心数据格式和数据操作方法的实现细节。第二个优势是效率：系统可以自由选择适当的查询执行计划，并有效实现特定分析方法，在处理时间和答案质量之间进行权衡。为了评估这项工作的结果，并邀请社区其他成员对这一领域做出贡献，PI和她的团队将基于真实和合成数据集制定一套性能基准。有关详细信息，请参阅项目网站https://www.cs.drexel.edu/dbgroup/db4pref

Preferences are orders among a collection of items attributed to a population of judges. Preference data comes in a variety of forms, such as ranked lists and pairwise comparisons, and is ubiquitous in a plethora of applications across different domains. Over the past decade, there has been a sharp increase in the volume of preference data, in the diversity of applications that use it, and in the richness of preference data analysis methods. Examples of applications include rank aggregation in genomic data analysis, management of votes in elections, and recommendation systems in e-commerce. The goal of this project is to streamline the management and analysis of preference data. Towards this goal the PI and her team will develop a framework called DB4Pref, providing support to computational and data scientists who work with preference data. Models, algorithms, data and software products developed as part of this project will be made publicly available. The work will have an impact on the scientific community, in particular on the analysis of functional genomics data, which is central to many areas of bioinformatics, and on social applications, where it will enable efficient and effective analysis of user preferences. The PI will involve graduate and undergraduate students in her research, and will continue to work with women and under-represented minorities. Both the process and the outcome of this research will be integrated into data management and data science courses taught by the PI.The work will adopt the relational database model, and will enrich it with extensions that are specialized for handling preference data. Specifically, the PI and her team will introduce a special type of a relation that is designed for preference data, and will propose composable operators on preference relations that can be embedded in SQL statements, for convenient reuse across applications. Scalable implementations of preference relations will be developed, along with analytics such as preference clustering and rank aggregation. Treating preferences, and preference analytics, as first-class citizen in a relational database, and making these available through (augmented) SQL queries, will brings two important advantages. The first is usability: since SQL queries are specified declaratively, users need not worry about data formats and implementation details of data manipulation methods. The second advantage is efficiency: the system is free to choose an appropriate query execution plan, and an efficient implementation of a specific analysis method, exploiting a trade-off between processing time and answer quality. To evaluate results of this work, and to invite contributions to this area by other members of the community, the PI and her team will develop a set of performance benchmarks based on real and synthetic datasets.For further information see the project website at https://www.cs.drexel.edu/dbgroup/db4pref