STTR Phase I: Integrating Online Analytical Processing (OLAP) and Ontologies to Discover Inconsistencies in Expectations for Supply and Demand

这个小企业技术转让（STTR）第一阶段项目旨在开发一个软件应用程序，使商业人士能够大大提高他们预测供需的能力。 Clados与斯坦福大学医学信息学的知识建模小组合作，提出了一项联合努力，以产生相关的聚合来调查供应和需求。 这项工作将分三个阶段进行：（1）从一组“训练”的投资研究报告中捕获OWL中的交互;（2）直接使用OWL表示法对所有交互产生有意义的在线分析处理（OLAP）聚合;并且，在本发明中，（3）通过捕获和聚合来自“控制”的交互来评估结果。商业人士非常注重撰写叙述性文章，以衡量潜在的供应和需求，从而指导他们的投资决策。近年来，商业人士发现在撰写这些叙述时有用的信息的数量和可用性急剧增加，使社会受益匪浅。然而，大多数人只使用最通用的工具来撰写这些叙述，主要是散文文档和电子表格，这些都不足以构建这样的卷。因此，社会有机会进一步改善资源分配。 联机分析处理技术可以极大地改善撰写叙述的过程，以衡量潜在的供应和需求。

This Small Business Technology Transfer (STTR) Phase I project aims to produce a software application that enables business people to dramatically improve their ability to forecast supply and demand. In collaboration with the Knowledge Modeling Group at Stanford Medical Informatics, Clados proposes a joint effort to produce relevant aggregation to investigate supply and demand. The effort will proceed in three stages: (1) Capture interactions in OWL from a "training" set of investment research reports; (2) Produce meaningful Online Analytical Processing (OLAP) aggregations on all interactions using the OWL representation directly; and, (3) Evaluate the results by capturing and aggregating interactions from a "control" set of reports.Business people focus heavily on composing narratives to gauge potential supply and demand to guide their investment decisions. In recent years, society has benefited from dramatic increases in volume and availability of information that business people find useful in composing these narratives. However, most people compose those narratives using only the most general tools, predominantly prose documents and spreadsheet tables, and these are insufficient to structure such volume. As a result, society has an opportunity to further improve resource allocation. OLAP technologies may dramatically improve the process of composing narratives to gauge potential supply and demand.