CIF21 DIBBs: User Driven Architecture for Data Discovery

CIF21 DIBB：用户驱动的数据发现架构

• 批准号: 1443070
• 负责人: Giridhar Manepalli
• 金额: $ 148.49万
• 依托单位: Corporation for National Research Initiatives (NRI)
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1443070&HistoricalAwards=false
• 关键词: CIF21; DIBBs; User; Driven; Architecture

The number, size, and availability of scientific datasets have grown enormously over the last few years. As scientific activity becomes more data intensive and collaborative, a key challenge for cross-disciplinary research will be discovery of diverse data sets, managed within distributed repositories and registries. Currently, discovery of information on the Internet is largely performed through automated approaches, characterized by web crawling and associated algorithms, or labor intensive indexing and categorization, such as the National Library of Medicine index for medical literature. There are significant amounts of data housed in repositories where only researchers with expertise in the specific field know and access the data.This project builds a user driven architecture for data discovery (UDADD), a capability that enhances discovery of scientific datasets by building a global index from diverse communities with minimal input. In the UDADD approach user actions, such as dataset queries or downloads, drive the construction of a global index. These actions are recorded and gathered automatically, through cooperation with repository managers. Two software plugins are provided to help the repositories interact with the UDADD system. The architecture includes ranking techniques based on frequency and recency of use of the datasets. The pilot architecture will be demonstrated and evaluated using cooperating repositories within the DataNet Federation Consortium. Currently, six science and engineering communities participate in the consortium, including national scale projects in oceanography, social science, cognitive science, hydrology, engineering, and plant biology.

在过去几年中，科学数据集的数量、规模和可用性都大幅增长。 随着科学活动变得更加数据密集和协作，跨学科研究的一个关键挑战将是发现在分布式存储库和注册表中管理的不同数据集。 目前，互联网上的信息发现主要通过自动化方法进行，其特点是网络爬行和相关算法，或劳动密集型索引和分类，例如国家医学图书馆的医学文献索引。 存储库中存储着大量数据，只有具有特定领域专业知识的研究人员才能了解和访问这些数据。该项目构建了一个用户驱动的数据发现架构 (UDADD)，该功能通过以最少的输入从不同社区构建全球索引来增强科学数据集的发现。 在 UDADD 方法中，用户操作（例如数据集查询或下载）驱动全局索引的构建。通过与存储库管理员的合作，这些操作会被自动记录和收集。提供了两个软件插件来帮助存储库与 UDADD 系统交互。该架构包括基于数据集使用频率和新近度的排名技术。该试点架构将使用 DataNet Federation Consortium 内的合作存储库进行演示和评估。 目前，有六个科学和​​工程界参与该联盟，包括海洋学、社会科学、认知科学、水文学、工程学和植物生物学领域的国家级项目。