CRI: Collaborative Research: Improving Experimental Computer Science with a Searchable Web Portal for Data Sets

CRI：协作研究：通过可搜索的数据集门户网站改进实验计算机科学

• 批准号: 0551597
• 负责人: Andrew McCallum
• 金额: $ 20万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-15 至 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0551597&HistoricalAwards=false
• 关键词: CRI; Collaborative; Research; Improving; Experimental

This collaborative project, developing and populating a Web-based Dataset Portal, provides a powerful front-end for online searching, querying, and browsing research datasets coupled to an intelligent back-end system that dynamically provides cross-references among datasets, research papers, techniques, authors, grants, and journals/conferences. The datasets are linked to Rexa, a research paper digital library at U Mass. The work redesigns the UCI dataset archive with structured meta information that allow queries on the web creating a formalized repository of research datasets with uniform queryable metadata. The system is built on the UCI Machine Learning and KDD Data Repositories. In research areas such as machine learning, data mining, applied statistics, language modeling, information retrieval, computer vision, and speech recognition, methodologies are often evaluated on publicly available datasets. Although these Datasets often serve as a common touchstone for communication, identifying and locating specific data spread haphazardly across various Web sites presents some difficulty. This work creates a community resource to address this problem.Broader Impact: The project directly impacts empirical research, teaching, and most collaborative research activities. Browsing data that suggest new models and applications should inspire researchers and students. Real world data sets not only broaden research but are also bound to encourage teachers to incorporate these in the curriculum. Sharing data should bring about more collaboration from multiple areas.

这个合作项目，开发和填充一个基于web的数据集门户，为在线搜索、查询和浏览研究数据集提供了一个强大的前端，与一个智能后端系统相结合，该系统动态地提供数据集、研究论文、技术、作者、资助和期刊/会议之间的交叉引用。这些数据集与麻省大学的研究论文数字图书馆Rexa相连。该工作用结构化元信息重新设计了UCI数据集归档，允许在web上查询，创建具有统一可查询元数据的形式化研究数据集存储库。该系统建立在UCI机器学习和KDD数据存储库的基础上。在机器学习、数据挖掘、应用统计学、语言建模、信息检索、计算机视觉和语音识别等研究领域，方法通常是在公开可用的数据集上进行评估的。尽管这些数据集经常用作通信的通用试金石，但是识别和定位随意分布在各种Web站点上的特定数据存在一些困难。这项工作创建了一个社区资源来解决这个问题。更广泛的影响：该项目直接影响实证研究、教学和大多数合作研究活动。浏览提出新模型和应用的数据应该会激励研究人员和学生。真实世界的数据集不仅拓宽了研究范围，而且必然会鼓励教师将这些数据纳入课程。共享数据应该会带来更多来自多个领域的协作。