Planning Grant: I/UCRC for Computation Intensive Big Data Analytics for Multimodal Temporal Prediction, Retrieval, and Attribution

规划拨款：I/UCRC 用于多模态时间预测、检索和归因的计算密集型大数据分析

• 批准号: 1464671
• 负责人: Ramakrishna Akella
• 金额: $ 1.45万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1464671&HistoricalAwards=false
• 关键词: Planning; Grant; UCRC; Computation; Intensive

Recently, both leading technological firms and academic institutions have developed a new capability to capture and process data at a scale many orders larger than previously possible. Firms, particularly in Silicon Valley, have demonstrated how this capability, when blended with Analytics, can be exploited to solve a host of new problems of great practical value. These range from Computational (or Online) Advertising, with immense commercial value being realized, to Healthcare Analytics, which is of tremendous and critical value societally. At our Research Site at the University of California, Santa Cruz, we focus on enhancing the predictions and searches based on Variety of data (in addition to Volume and Velocity), since data such as Electronic Healthcare Records (EHRs) include not just numerical data (about vitals and labs), but also text (notations by doctors and nurses), images (X-rays etc.), video (such as a doctor?s examination), body sensors, etc. We also explore and exploit ways of identifying more informative data on-the-go, as well as identifying effective ways to speed up and slow down the rate of obtaining this informative data automatically based on need and context, to achieve superior prediction. Finally, we develop new ways of evaluating the true impact of each data type/source on the desired outcome. The fundamental discoveries will concern methods for determining the value of each type or source of data in more effective prediction (and search) of dynamic system state and intervention decisions.Our research concerns analyzing multi-type and source data for enhanced prediction, search, and decision making. Our collaborative research site focuses on research and development of novel scalable Big Data Analytics (BDA) solutions for multimodal data and streaming data suitable for various computing architectures (where the knowledge extracted from multiple modes is far greater than the sum of knowledge discovered from individual data-types); and novel interoperable solutions to combine analytics tools and solutions. Specific sub-thrusts include:1. Scalable temporal prediction/classification, including recommenders, for multi-type data, incorporating latent business processes and ontologies.2. Bayesian interactive information retrieval for massive data sets, combined with extraction incorporating latent business processes and ontologies in mining data.3. Data type and source characterization in terms of power of prediction/classification/retrieval, including causality in marketplaces and temporal aspects.4. Interoperable solutions for BDA that enable seamless and efficient knowledge discovery from multimodal data involving different solutions and tools in different languages and different APIs, and Programmable File and Storage Systems for Big Data Analytics.5. User behavioral modeling analytics and the analytics/economics of decisions and interventions.The domains and contexts we propose to explore include a subset of: system health (e.g. aviation safety, jet engine maintenance) and analytic services based on heterogeneous data and data/text mining, extraction, and retrieval for service centers (e.g. in network health or financial, sales and marketing services), principled knowledge discovery for personalized healthcare and web analytics, diagnostics and prognostics in Internet of Things.

最近，领先的技术公司和学术机构都开发了一种新的能力，可以捕获和处理比以前大许多数量级的数据。公司，特别是硅谷的公司，已经证明了这种能力，当与分析相结合时，可以用来解决一系列具有巨大实用价值的新问题。这些范围从具有巨大商业价值的计算（或在线）广告到具有巨大社会价值的医疗保健分析。在我们位于圣克鲁斯的加州大学的研究中心，我们专注于增强基于各种数据（除了体积和速度）的预测和搜索，因为电子医疗记录（EHR）等数据不仅包括数字数据（关于生命体征和实验室），还包括文本（医生和护士的注释），图像（X射线等），视频（如医生？我们还探索和利用在移动中识别更多信息数据的方法，以及识别有效的方法来根据需要和上下文自动加快和减慢获取这些信息数据的速度，以实现上级预测。最后，我们开发了新的方法来评估每个数据类型/源对预期结果的真正影响。基本的发现将涉及确定每种类型或数据源的价值的方法，以更有效地预测（和搜索）动态系统状态和干预决策。我们的研究涉及分析多类型和源数据，以增强预测，搜索和决策。我们的合作研究中心专注于研究和开发适用于各种计算架构的多模式数据和流数据的新型可扩展大数据分析（BDA）解决方案（从多种模式中提取的知识远远大于从单个数据类型中发现的知识之和）;以及新颖的可互操作解决方案，以结合联合收割机分析工具和解决方案。具体的子推力包括：1.可扩展的时间预测/分类，包括多类型数据的分类器，结合潜在的业务流程和本体。2.面向海量数据集的贝叶斯交互式信息检索，结合挖掘数据中潜在业务流程和本体的提取.从预测/分类/检索能力的角度对数据类型和源进行表征， 包括市场和时间方面的因果关系。面向BDA的可互操作解决方案，能够从多模态数据中无缝、高效地发现知识，涉及不同语言和不同API的不同解决方案和工具，以及面向大数据分析的可编程文件和存储系统。5.用户行为建模分析以及决策和干预的分析/经济学。我们建议探索的领域和背景包括以下子集：系统健康（例如航空安全、喷气发动机维护）和基于服务中心的异构数据和数据/文本挖掘、提取和检索的分析服务（例如，在网络健康或金融，销售和营销服务），个性化医疗保健和网络分析的原则性知识发现，物联网中的诊断和诊断。