CRII: III: Managing Preference Data

CRII：III：管理偏好数据

• 批准号: 1464327
• 负责人: Julia Stoyanovich
• 金额: $ 17.49万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1464327&HistoricalAwards=false
• 关键词: CRII; III; Managing; Preference; Data

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

偏好是归因于法官人口的项目集合之间的命令。偏好数据有多种形式，例如排名列表和成对比较，并且在跨不同域的大量应用中无处不在。 在过去的十年中，在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中，偏好数据的数量急剧增加。申请的示例包括基因组数据分析中的等级汇总，选举中的投票管理以及电子商务的推荐系统。 该项目的目的是简化首选项数据的管理和分析。 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，为与偏好数据合作的计算和数据科学家提供支持。将公开可用的模型，算法，数据和软件产品作为该项目的一部分。这项工作将对科学界有影响，特别是对功能基因组数据的分析，这对生物信息学的许多领域以及社交应用是至关重要的，它将能够对用户偏好进行有效的有效分析。 PI将涉及研究生和本科生的研究，并将继续与妇女和代表不足的少数民族合作。这项研究的过程和结果都将集成到PI教授的数据管理和数据科学课程中。工作将采用关系数据库模型，并将以专门用于处理偏好数据的扩展名来丰富它。具体来说，PI和她的团队将引入一种专为偏好数据而设计的特殊类型的关系，并将在SQL语句中嵌入可嵌入的偏好关系，以便在应用程序跨应用程序中方便地重复使用。 将开发偏好关系的可扩展实现，以及诸如偏好聚类和等级聚合之类的分析。将偏好和偏好分析作为关系数据库中的一流公民，并通过（增强）SQL查询提供这些优势，将带来两个重要的优势。首先是可用性：由于声明地指定了SQL查询，因此用户不必担心数据格式和数据操纵方法的实现详细信息。 第二个优点是效率：系统可以自由选择适当的查询执行计划，并有效地实施特定的分析方法，从而利用处理时间和答案质量之间的权衡。为了评估这项工作的结果，并为社区的其他成员邀请对这一领域的贡献，PI和她的团队将基于真实和合成数据集开发一套性能基准。有关更多信息，请参见项目网站https://wwwww.cs.cs.drexel.educs.drexel.edu/dbgrup/dbgroup/dbroup/db4pref/db4pref。

项目成果

A Query Engine for Probabilistic Preferences

概率偏好的查询引擎

• DOI: 10.1145/3183713.3196923
• 发表时间: 2018
• 期刊: ACM SIGMOD
• 作者: Cohen, Uzi;Kenig, Batya;Ping, Haoyue;Kimelfeld, Benny;Stoyanovich, Julia
• 通讯作者: Stoyanovich, Julia