CAREER: Efficient Discovery of Previously Unknown Patterns and Relationships in Massive Time Series Databases

职业：在海量时间序列数据库中有效发现以前未知的模式和关系

• 批准号: 0237918
• 负责人: Eamonn Keogh
• 金额: $ 40万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0237918&HistoricalAwards=false
• 关键词: CAREER; Efficient; Discovery; Previously; Unknown

To date, the vast majority of research on time series data mining has focused on similarity search and to a lesser extent on clustering. However, from a knowledge discovery viewpoint, there are several important unsolved problems in time series data mining that are more interesting, important, and challenging. This project addresses these problems. The long-term goal is the creation of efficient algorithms to allow the extraction of knowledge in the form of patterns, anomalies, regularities and rules, from massive time series datasets. Because of the ubiquity of times series data, the work may have benefits in areas as diverse as cardiology, industry, astronomy, medicine, bioinformatics, finance, meteorology, entertainment and networking. Local collaborators in industry and science, who will test the algorithms, have been identified. To enhance broader impacts of this project, results will be disseminated by an expansion of the UCR Time Series Data Mining Archive, which will make all code, datasets and publications created by the project freely available to data mining researchers and practitioners. The Web site (http://www.cs.ucr.edu/~eamonn/NSFcareer/NSF.html) provides more information about this project.A special feature of the project is an effort involving undergraduate and graduate students to find, implement and compare relevant work to the approach developed in this project. Time series data analysis is very important for business and thus the project will have broad impact beyond its scientific impact.

到目前为止，时间序列数据挖掘的绝大多数研究都集中在相似性搜索上，在较小程度上集中在聚类上。然而，从知识发现的角度来看，在时间序列数据挖掘中有几个重要的未解决的问题是更有趣的，重要的，和具有挑战性的。这个项目解决了这些问题。长期目标是创建有效的算法，以允许从大量时间序列数据集中提取模式，异常，规则和规则形式的知识。由于时间序列数据的普遍存在，这项工作可能在心脏病学、工业、天文学、医学、生物信息学、金融、气象学、娱乐和网络等不同领域都有好处。已经确定了工业和科学领域的当地合作者，他们将测试这些算法。为了扩大该项目的影响，将通过扩大UCR时间序列数据挖掘档案来传播成果，这将使数据挖掘研究人员和从业人员免费获得该项目创建的所有代码、数据集和出版物。该网站（http：//www.cs.ucr.edu/caseamonn/NSFcareer/NSF.html）提供了关于该项目的更多信息。该项目的一个特点是，本科生和研究生努力寻找、实施和比较与该项目开发的方法有关的工作。时间序列数据分析对商业非常重要，因此该项目将产生超出其科学影响的广泛影响。