SBIR Phase I: Relational Bayesian Modeling for Electronic Commerce

SBIR 第一阶段：电子商务的关系贝叶斯建模

• 批准号: 0231961
• 负责人: Bruce D'Ambrosio
• 金额: $ 9.98万
• 依托单位: ESHOPPERTOOLS.COM INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-01-01 至 2003-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0231961&HistoricalAwards=false
• 关键词: SBIR; Phase; Relational; Bayesian; Modeling

This Small Business Innovation Research Phase I Project will advance Relational Bayesian Modeling (RBM) technology, with specific focus on Information-based Technology applications in electronic commerce, especially customer and application-system behavior modeling. Relational Bayesian modeling combines recent advances in graphical statistical modeling with modern relational and object-oriented data models. An RBM is compact, precise, and efficient. It is compact because structural information, represented graphically, identifies a minimal set of numeric information needed to complete the model. It is precise because any probabilistic model, of arbitrary complexity, can be represented. No global simplifying assumptions are made. Finally, the structural information can be exploited to support efficient computation with the model. Work under this SBIR will focus an open problem in RBMs: the representation and discovery of, and computation with, probabilistic dependencies in many-to-one relations. Preliminary research has shown that relational Bayesian models can effectively capture dynamic behavior, and can be applied to recognize behavioral profiles in real-time, of both users and systems. This will enable a new generation of adaptive web interfaces, improve the reliability and predictability of multi-tier system performance, and be an important element in realizing autonomic computing. The commercial application for this technology is electronic commerce which is in need of the improve reliability and predictability of multi-tier systems which this solution will provide.

该小型企业创新研究第一阶段项目将推进关系贝叶斯建模 (RBM) 技术，特别关注电子商务中基于信息的技术应用，特别是客户和应用系统行为建模。关系贝叶斯建模将图形统计建模的最新进展与现代关系和面向对象的数据模型结合起来。 RBM 紧凑、精确且高效。它是紧凑的，因为以图形方式表示的结构信息标识了完成模型所需的最小数字信息集。它是精确的，因为可以表示任意复杂度的任何概率模型。没有做出全局简化假设。最后，可以利用结构信息来支持模型的有效计算。该 SBIR 下的工作将重点关注 RBM 中的一个开放问题：多对一关系中概率依赖关系的表示、发现和计算。初步研究表明，关系贝叶斯模型可以有效捕获动态行为，并可用于实时识别用户和系统的行为概况。这将实现新一代自适应网络界面，提高多层系统性能的可靠性和可预测性，并成为实现自主计算的重要元素。 该技术的商业应用是电子商务，需要提高该解决方案所提供的多层系统的可靠性和可预测性。