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

SBIR 第二阶段：用于决策知识管理的数据聚合、关联、分析和趋势分析软件

• 批准号: 0848718
• 负责人: Aaron Kopel
• 金额: --
• 依托单位: Chatterspike, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-04-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0848718&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Software; Aggregate

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.

这个小企业创新研究（SBIR）第二阶段项目解决了寻求从大量在线“聊天”（来自各种来源的在线内容，如博客，行业重点网站和媒体生成的材料）中获得可靠和可操作信息的实体的挑战。第二阶段将侧重于技术目标，以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准备。他们的成就将需要设计，开发和实施新颖的，以利基为重点的算法，这将使挖掘和评估成千上万的在线资源和生产的数据与可量化的质量指标有关的权威性，可靠性，影响力和情绪。最终的产品将通过算法确定并实时真实的定量测量和评估这些参数，同时挖掘在线数据源，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，所产生的技术将能够生成自动化、高度定制的详细报告，并具有高度的定量确认可靠性。这种能力将来自于创造新的算法，旨在利用尖端的理论方法从多种在线来源中提取，验证和评估信息。这些报告将上级目前可用的技术和方法产生的人工报告。 此外，如果成功，该技术将产生重大的社会效益。公司将能够更快地做出反应，以满足消费者的需求，并纠正消费者意见中的负面趋势。该技术还将能够在非常早期的阶段可靠地检测趋势;在某些情况下，在趋势变得明显并被其他方法检测到之前的几周或几个月。