Collaborative Research: Learning Classifiers From Autonomous, Semantically Heterogeneous, Distributed Data

协作研究：从自治、语义异构、分布式数据中学习分类器

• 批准号: 0711356
• 负责人: Samik Basu
• 金额: $ 30.45万
• 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: 2007
• 资助国家: 美国
• 起止时间: 2007-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0711356&HistoricalAwards=false
• 关键词: Collaborative; Research; Learning; Classifiers; Autonomous

Advances in networks, sensors, storage, computing, and high throughput data acquisition, have led to a proliferation of autonomous, distributed data sources in many areas of human activity. New discoveries in biological, physical, and social sciences and engineering are being driven by our ability to discover, share, integrate and analyze disparate types of data. Statistically-based machine learning algorithms offer some of the most cost-effective approaches to discovery of experimentally testable predictive models and hypotheses from data. However, the large size, distributed nature, and autonomy of the data sources (and the attendant differences in access, queries allowed, processing capabilities, structure, organization, and underlying data models and data semantics) present hurdles to effective utilization of machine learning. This research aims to overcome these hurdles by developing efficient, resource-aware distributed algorithms and software services to support collaborative, integrative knowledge acquisition such a setting. The research team will implement, deploy, and evaluate the resulting algorithms using benchmark data sets, associated data models and ontologies, and user-specified inter-ontology mappings on a distributed test-bed of networked databases and services at Iowa State University and Kansas State University. The resulting open-source software can potentially transform collaborative e-science in the same way that Web has transformed information sharing. Broader impacts of this research include enhanced opportunities for research-based training of graduate and undergraduate students, interdisciplinary collaborations, participation of under-represented groups, and development of increasingly sophisticated software to support collaborative, integrative e-science. The project web site (http://www.cild.iastate.edu/projects/indus.html) provides access to information about the project, benchmark data, publications, software, and documentation.

网络，传感器，存储，计算和高吞吐量数据的进步导致了许多人类活动领域的自主，分布式数据源的扩散。 生物，物理和社会科学和工程学方面的新发现是由我们发现，共享，整合和分析不同数据类型的能力所驱动的。 基于统计的机器学习算法为发现实验可测试的预测模型和数据中的假设提供了一些最具成本效益的方法。但是，数据源的较大规模，分布式性质和自治（以及访问，允许查询，处理能力，结构，组织以及基础数据模型和数据语义的差异）呈现出有效利用机器学习的障碍。这项研究旨在通过开发高效，资源感知的分布式算法和软件服务来克服这些障碍，以支持协作，综合知识获取这样的设置。研究团队将使用基准数据集，关联的数据模型和本体学以及在爱荷华州立大学和堪萨斯州立大学的网络数据库和服务的分布式测试中实施，部署和评估所得算法。 由此产生的开源软件可以像Web转换信息共享一样改变协作的电子科学。这项研究的更广泛的影响包括对研究生和本科生进行基于研究的培训的机会，跨学科合作，代表性不足的群体的参与以及开发日益成熟的软件以支持协作，综合的电子科学。项目网站（http://www.cild.iastate.edu/projects/indus.html）提供有关项目，基准数据，出版物，软件和文档的信息的访问。