ITR: Algorithms and Software for Knowledge Acquisition from Heterogeneous Distributed Data

ITR：从异构分布式数据获取知识的算法和软件

• 批准号: 0219699
• 负责人: Vasant Honavar
• 金额: --
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-15 至 2006-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0219699&HistoricalAwards=false
• 关键词: ITR; Algorithms; Software; Knowledge; Acquisition

Development of high throughput data acquisition technologies together with advances in computing, and communications have resulted in an explosive growth in the number, size, and diversity of potentially useful information sources. However, the massive size, heterogeneity, autonomy, and distributed nature of the data repositories present significant hurdles in extracting knowledge from this data. This research seeks to overcome these hurdles through the design, analysis, and implementation of:a) Efficient distributed and cumulative learning algorithms with provable performance guarantees (relative to their centralized or batch counterparts) for knowledge acquisition from distributed data sources;b) Customizable information extraction agents that can effectively exploit domain or context-specific ontologies supplied by the users to extract the information needed for learning (e.g., statistics) from distributed data sources despite differences in query capabilities, interfaces, ontologies, and access restrictions to facilitate analysis of heterogeneous distributed data from different perspectives.c) INDUS - a test-bed for knowledge acquisition from heterogeneous distributed data in computational molecular biology (e.g., characterization of protein sequence-structure-function relationships using diverse sources of biological data).The resulting algorithms and software can accelerate, potentially by an order of magnitude, the rate of scientific

高吞吐量数据采集技术的发展以及计算和通信的进步导致了潜在有用信息源的数量、规模和多样性的爆炸性增长。然而，数据存储库的巨大规模、异构性、自治性和分布式性质给从这些数据中提取知识带来了重大障碍。本研究旨在通过设计、分析和实现来克服这些障碍：a）高效的分布式和累积学习算法，具有可证明的性能保证（相对于集中式或批量算法），用于从分布式数据源获取知识；b）可定制的信息提取代理可以有效地利用用户提供的领域或上下文特定的本体，从分布式数据源中提取学习所需的信息（例如统计数据），尽管查询能力、接口、本体和访问限制存在差异，以促进分析c) INDUS - 计算分子生物学中从异构分布式数据获取知识的测试平台（例如，使用不同来源的生物数据来表征蛋白质序列-结构-功能关系）。由此产生的算法软件可以加速科学发展的速度，有可能提高一个数量级