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以及计算进步的开发以及通信导致了潜在有用信息源的数量，大小和多样性的爆炸性增长。但是，数据存储库的巨大规模，异质性，自主性和分布性质在从该数据中提取知识方面构成了重大障碍。这项研究旨在通过以下设计，分析和实施来克服这些障碍：a）具有可证明的性能保证的有效分布式和累积学习算法（相对于其集中式或批处理的相对于分布式数据来源获取的知识获取的可证明性能保证；从分布式数据源中，尽管查询能力，界面，本体论和访问限制有所不同，以促进从不同角度分析异质分布数据的分析。软件可以加速，有可能通过一个数量级，科学的速度