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

高通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、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、自治性和分布式特性给从这些数据中提取知识带来了巨大的障碍。本研究试图通过设计、分析和实现以下内容来克服这些障碍：a)高效的分布式和累积式学习算法，具有可证明的性能保证(相对于集中式或批处理对应的)，用于从分布式数据源获取知识；B)可定制的信息提取代理，其可以有效地利用由用户提供的领域或上下文特定的本体，以从分布式数据源提取学习(例如，统计)所需的信息，尽管查询能力、接口、本体和访问限制不同，以便于从不同的角度分析不同的分布式数据。c)Indus-用于从计算分子生物学中的不同的分布式数据获取知识的试验台(例如，使用不同的生物数据源表征蛋白质序列-结构-功能关系)。由此产生的算法和软件可以潜在地将科学化的速度加快一个数量级

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