SBIR Phase II: Software to Aggregate, Correlate, Analyze and Trend data for Knowledge Management in Decision Making

该小型企业创新研究(SBIR)第二阶段项目旨在应对实体所面临的挑战，这些实体寻求从大量的在线“闲聊”(来自各种来源的在线内容，如博客、以行业为中心的网站和媒体生成的材料)中获取可靠和可操作的信息。第二阶段将侧重于技术目标，这些目标将提高第一阶段研究的ChatterSpike概念产生的信息的质量和可靠性。这些目标分为三类：数据清理、上下文分析和基本商业就绪。他们的成就将需要设计、开发和实施新的、专注于利基市场的算法，这些算法将能够挖掘和评估数千个在线来源，并产生具有与权威、可靠性、影响力和情绪有关的可量化质量指标的数据。所产生的产品将在挖掘在线数据来源的过程中实时通过算法确定并定量测量和评估这些参数，验证其结论，并在每次执行检索操作时重新验证这些结论。通过专注于特定的行业利基市场，所产生的技术将能够以高度定量确认的可靠性生产自动化、高度定制的详细报告。这一能力将来自于设计新算法的结果，该算法旨在利用尖端理论方法从多种在线来源提取、验证和评估信息。这些报告将优于目前可用的技术和方法编制的人工报告。此外，如果成功，这项技术将产生显著的社会效益。公司将能够更快地做出反应，满足消费者的需求，纠正消费者意见的负面趋势。这项技术还将能够在非常早期的阶段可靠地检测到趋势；在某些情况下，趋势在变得明显之前几周或几个月就能被其他方法检测到。

This Small Business Innovation Research (SBIR) Phase II project addresses the challenges for entities seeking to derive reliable and actionable information from enormous quantities of online "chatter" (online content from a variety of sources such as blogs, industry-focused sites, and media-generated material). Phase II will focus on technical objectives that will enhance the quality and reliability of the information produced by the ChatterSpike concept researched in Phase I. These objectives fall into three categories: data cleansing, context analysis, and basic commercial readiness. Their achievement will require the design, development and implementation of novel, niche-focused algorithms that will enable the mining and evaluation of thousands of online sources and the production of data with quantifiable quality metrics relating to authority, reliability, influence, and sentiment. The resulting product will algorithmically determine and quantitatively measure and evaluate these parameters in real time as it mines online sources for data, validating its conclusions and re-validating them every time it performs a retrieval operation.By focusing on specific industry niches, the technology produced will enable the production of automated, highly tailored, detailed reports with a high degree of quantitatively-confirmed reliability. This capability will result from the creation of novel algorithms designed to exploit cutting-edge theoretical approaches to extracting, validating, and evaluating information from a multiplicity of online sources. These reports will be superior to the manual reports produced by currently available technologies and approaches. In addition, if successful, the technology will have significant societal benefit. Companies will be able to react more quickly to meet consumer demands and to correct negative trends in consumer opinions. The technology will also be able to detect trends reliably at a very early stage; in some cases weeks or months before they become obvious and are detected by other methods.