III: Small: A Scalable Search Tool for Interesting Patterns in Scientific Data

III：Small：科学数据中有趣模式的可扩展搜索工具

• 批准号: 1017793
• 负责人: Mirek Riedewald
• 金额: $ 49.92万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017793&HistoricalAwards=false
• 关键词: III; Small; Scalable; Search; Tool

Across many scientific disciplines, the availability of very largeamounts of data is creating a paradigm shift. The goal of this projectis to develop Scolopax, a tool for finding interesting patterns inclassification and prediction models trained on large high-dimensionaldata. These patterns can capture previously unknown relationshipsbetween the variables of a complex process, hence are essential forexploratory analysis and scientific discovery.This project explores several research directions to lay thefoundations for Scolopax: (1) Design of a new query language that canexpress all common pattern search preferences. (2) Algorithms forlearning a query so that even non-technical users can formulatenon-trivial queries through an interactive process. (3) New rewriterules and efficient data management approaches to automaticallytransform queries into fast implementations on a cluster or Cloud. (4)Design of new semi-parametric data mining techniques that are amenableto scalable training, evaluation, and pattern confidence computation.User-friendly query writing functionality makes Scolopax accessible toscientists and citizen scientists alike. Its planned deploymentthrough popular Web sites, e.g., those hosted by the Cornell Lab ofOrnithology, has the potential to enable new scientificdiscoveries. By letting citizen scientists not only contribute data,but also make their own discoveries using the data, Scolopax alsoserves as an important enabler and motivator for outreach programs andgreater involvement of citizen scientists. For further information seethe project web site at the URL:http://www.ccs.neu.edu/home/mirek/Projects/Scolopax

在许多科学学科中，大量数据的可用性正在创造一种范式转变。该项目的目标是开发Scolopax，这是一种用于在大型高维数据上发现有趣的模式分类和预测模型的工具。这些模式可以捕捉复杂过程中变量之间的未知关系，因此对于探索性分析和科学发现是必不可少的，本项目探索了几个研究方向，为Scolopax奠定了基础：（1）设计一种新的查询语言，可以表达所有常见的模式搜索偏好。(2)学习查询的算法，这样即使是非技术用户也可以通过交互过程来制定非平凡的查询。(3)新的重写规则和高效的数据管理方法，可自动将查询转换为集群或云上的快速实现。（4）设计了新的半参数数据挖掘技术，这些技术适用于可扩展的训练、评估和模式置信度计算。用户友好的查询编写功能使Scolopax对科学家和公民科学家都很容易使用。它计划通过流行的网站进行部署，这些由康奈尔鸟类学实验室主持的项目，有可能带来新的科学发现。通过让公民科学家不仅贡献数据，而且还使用数据进行自己的发现，Scolopax也是推广计划和公民科学家更多参与的重要推动者和动力。欲了解更多信息，请访问项目网站：http://www.ccs.neu.edu/home/mirek/Projects/Scolopax