SGER: An Online Repository of Large Data Sets for Data Mining Research and Experimentation

SGER：用于数据挖掘研究和实验的大型数据集在线存储库

• 批准号: 9813584
• 负责人: Padhraic Smyth
• 金额: $ 9.97万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-08-15 至 2001-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9813584&HistoricalAwards=false
• 关键词: SGER; Online; Repository; Large; Data

Over the last two decades there has been an explosive growth in online data storage of various forms. These large datasets have motivated the rapid development of data mining methods. However, until now, there has been a lack of an online repository of large data sets for researchers to evaluate and compare their methods. In this project, an online repository of large and difficult data sets are being gathered that are representative of the diverse character of many important scientific and business domains. This repository includes high-dimensional data sets as well as data sets of different data types (time series, spatial data, transaction data, and so forth). The primary role of the repository is that of a benchmark testbed to enable researchers in data mining (including computer scientists, statisticians, engineers, and mathematicians) to scale existing and future data analysis algorithms to very large data sets. Each data set in the respository contains online documentation, metadata, and links to relevant background domain information such as prior published work. Availability of a standard set of large benchmark data sets will directly stimulate and foster systematic progress in data mining related research, similar to the affect that the UCI Machine Learning Data Repository has had on machine learning research. This repository will play a substantial role in brokering the gap between research-oriented algorithm development in the laboratory and the real-world practicalities and challenges of very large data sets. http://www.ics.uci.edu/~mlearn/MLRepository.html

在过去的二十年里，各种形式的在线数据存储出现了爆炸性增长。这些大型数据集促进了数据挖掘方法的快速发展。然而，到目前为止，一直缺乏一个大型数据集的在线存储库，供研究人员评估和比较他们的方法。在这个项目中，正在收集一个大型和困难的数据集的在线储存库，这些数据集代表了许多重要科学和商业领域的不同特点。该存储库包括高维数据集以及不同数据类型的数据集（时间序列、空间数据、事务数据等）。存储库的主要作用是基准测试平台，使数据挖掘研究人员（包括计算机科学家，统计学家，工程师和数学家）能够将现有和未来的数据分析算法扩展到非常大的数据集。资源库中的每个数据集都包含在线文档、元数据和相关背景领域信息的链接，如以前发表的作品。一组标准的大型基准数据集的可用性将直接刺激和促进数据挖掘相关研究的系统性进展，类似于UCI机器学习数据库对机器学习研究的影响。这个知识库将在填补实验室中面向研究的算法开发与现实世界的实用性和超大数据集的挑战之间的差距方面发挥重要作用。 http://www.ics.uci.edu/~mlearn/MLRepository.html