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

高吞吐量数据采集技术的发展以及计算和通信的进步已经导致潜在有用信息源的数量、大小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、分析和实现以下内容来克服这些障碍：a）有效的分布式和累积学习算法，具有可证明的性能保证（相对于其集中式或批处理算法），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本体来提取学习所需的信息（例如，统计），以便于从不同的视角分析异质分布式数据。c）INDUS -用于从计算分子生物学中的异质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学数据表征蛋白质序列-结构-功能关系）。由此产生的算法和软件可以加速，可能是一个数量级，

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