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

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可能大得多的数据。公司，特别是硅谷的公司，已经证明了当这种能力与Analytics相结合时，可以如何利用它来解决一系列具有重大实用价值的新问题。它们的范围从实现巨大商业价值的计算性(或在线)广告，到具有巨大和关键社会价值的医疗分析。在我们位于加州大学圣克鲁斯分校的研究站点，我们专注于增强基于各种数据的预测和搜索(除了体积和速度)，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(医生和护士的记号)、图像(X光等)、视频(如医生？S检查)、身体传感器等。我们还探索和开发在移动中识别更多信息数据的方法，以及确定有效的方法来加快和减慢根据需要和背景自动获得这些信息性数据的速度，以实现更好的预测。最后，我们开发了新的方法来评估每种数据类型/源对期望结果的真实影响。我们的基本发现将涉及在动态系统状态和干预决策的更有效预测(和搜索)中确定每种类型或数据源的价值的方法。我们的研究涉及分析多种类型和来源的数据，以增强预测、搜索和决策。我们的协作研究网站专注于研究和开发适用于各种计算架构(其中从多个模式提取的知识远远大于从单个数据类型发现的知识总和)的适用于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；以及结合分析工具和解决方案的新型可互操作解决方案。具体的次级任务包括：1.可扩展的时间预测/分类，包括推荐器，用于多种类型的数据，纳入潜在的业务流程和本体论。面向海量数据集的贝叶斯交互信息检索，结合挖掘数据中潜在业务流程和本体的抽取。预测/分类/检索能力方面的数据类型和来源表征，包括市场和时间方面的因果关系。BDA的可互操作解决方案，支持从涉及不同语言和不同API的不同解决方案和工具的多模式数据无缝高效地发现知识，以及用于大数据分析的可编程文件和存储系统。用户行为建模分析和决策和干预的分析/经济学。我们建议探索的领域和背景包括：系统健康(例如航空安全、喷气发动机维护)和基于异类数据和数据/文本的分析服务的子集，服务中心(例如网络健康或金融、销售和营销服务)的挖掘、提取和检索，用于个性化医疗保健和网络分析的原则性知识发现，物联网中的诊断和预测。

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.