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

网络、传感器、存储、计算和高吞吐量数据采集的进步已经导致了人类活动的许多领域中的自主分布式数据源的激增。 生物、物理、社会科学和工程领域的新发现正受到我们发现、共享、整合和分析不同类型数据的能力的推动。 基于统计的机器学习算法提供了一些最具成本效益的方法来从数据中发现实验可测试的预测模型和假设。然而，数据源的大规模、分布式性质和自主性（以及随之而来的访问、允许的查询、处理能力、结构、组织和底层数据模型和数据语义的差异）给有效利用机器学习带来了障碍。本研究的目的是克服这些障碍，开发高效的，资源感知的分布式算法和软件服务，以支持协作，综合知识获取这样的设置。该研究小组将实施，部署，并使用基准数据集，相关的数据模型和本体，以及用户指定的本体间映射的分布式测试床上的网络数据库和服务在爱荷华州州立大学和堪萨斯州州立大学的算法进行评估。 由此产生的开源软件可以潜在地改变协作电子科学，就像Web改变信息共享一样。这项研究的更广泛的影响包括增强研究生和本科生的研究培训机会，跨学科合作，代表性不足的群体的参与，以及开发越来越复杂的软件，以支持协作，综合电子科学。该项目网站（http：//www.cild.iastate.edu/projects/indus.html）提供有关该项目、基准数据、出版物、软件和文档的信息。

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.