CAREER: Runtime Recommender Systems for Compositional Modeling of Scientific Computations

职业：用于科学计算组合建模的运行时推荐系统

• 批准号: 9984317
• 负责人: Naren Ramakrishnan
• 金额: $ 22.98万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-15 至 2005-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9984317&HistoricalAwards=false
• 关键词: CAREER; Runtime; Recommender; Systems; Compositional

ABSTRACTEIA 9984317Naren RamakrishnanVirginia Polytechnic Institute & State UniversityCAREER: Runtime Recommender Systems for Compositional Modeling of Scientific ComputationsThe central goal of this career development proposal is to introduce runtime recommendation, an abstraction that extends the above two ideas significantly. Specifically, it monitors a computational process, detects state-changes, and makes selections of solution components dynamically, thus aiding knowledge-based application composition at runtime. Such a facility is important in many problem domains because: (i) the nature of the problem being solved changes as the computations are being performed, (ii) the underlying computing platform or resource availability is dynamic, or (iii) information about application performance characteristics is acquired during the actual computation rather than before. While traditional recommenders are designed off-line (by organizing a battery of benchmark problems and algorithm executions, and subsequently mining it to obtain high-level recommendation rules), the design of a runtime recommender system is difficult, because such a database is not readily available and needs to be "captured" on the fly. Thus, a runtime recommender interacts dynamically with its environment and learns through interactions with its environment.

摘要9984317Naren Ramakrishnan弗吉尼亚理工学院&州立大学这个职业发展建议的中心目标是引入运行时推荐，这是一个显著扩展上述两个想法的抽象。 具体来说，它监视计算过程，检测状态变化，并动态地选择解决方案组件，从而在运行时帮助基于知识的应用程序组合。 这样的设施在许多问题领域中是重要的，因为：（i）被解决的问题的性质随着计算的执行而改变，（ii）底层计算平台或资源可用性是动态的，或者（iii）关于应用性能特性的信息是在实际计算期间而不是之前获取的。 虽然传统的推荐系统是离线设计的（通过组织一组基准问题和算法执行，并随后对其进行挖掘以获得高级推荐规则），但运行时推荐系统的设计是困难的，因为这样的数据库并不容易获得，并且需要在运行中“捕获”。 因此，运行时推荐器动态地与其环境交互，并通过与其环境的交互来学习。