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

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: 0551510
• 负责人: Padhraic Smyth
• 金额: $ 19.99万
• 依托单位: University of California-Irvine
• 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=0551510&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，提供了一个强大的前端在线搜索，查询和浏览研究数据集耦合到一个智能后端系统，动态提供数据集，研究论文，技术，作者，赠款和期刊/会议之间的交叉引用。这些数据集链接到Rexa，这是马萨诸塞大学的一个研究论文数字图书馆。这项工作重新设计了UCI数据集档案与结构化的Meta信息，允许在网络上查询创建一个正式的研究数据集与统一的可查询元数据库。该系统建立在UCI机器学习和KDD数据库之上。在机器学习、数据挖掘、应用统计、语言建模、信息检索、计算机视觉和语音识别等研究领域，方法学通常在公开可用的数据集上进行评估。虽然这些数据集通常作为交流的共同试金石，但识别和定位在各种网站上随意传播的特定数据存在一些困难。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。 浏览那些提出新模型和应用的数据应该会激励研究人员和学生。真实的世界数据集不仅拓宽了研究领域，而且必将鼓励教师将这些数据集纳入课程。共享数据应该带来来自多个领域的更多合作。