CIF: Small: Online Algorithms for Streaming Structured Big-Data Mining

CIF：小型：流式结构化大数据挖掘在线算法

• 批准号: 1526870
• 负责人: Namrata Vaswani
• 金额: $ 44.24万
• 依托单位: Iowa State University
• 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: 2015-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526870&HistoricalAwards=false
• 关键词: CIF; Small; Online; Algorithms; Streaming

In today's big data age, a lot of streaming big-data is generated around us. Most of this is either not stored or stored only for short periods of time, e.g., streaming videos or changing social network connections. This project develops novel online algorithms for dimensionality reduction and structural information recovery from incomplete or distorted data.This research makes several contributions to both the theory and the practice of structure recovery from streaming big-data. (1) The investigator and her team are developing provably correct online algorithms for robust structure (subspace or support) tracking from undersampled, outlier-corrupted or otherwise highly noisy streaming data. While batch approaches for these problems have been well-studied, the online problem is largely open. Our theoretical results are among the first correctness results for the robust subspace tracking (online robust PCA) and robust support tracking problems. Online algorithms are useful because they are faster and need lesser storage compared to most batch techniques. Moreover, our online algorithms remove a key limitation of batch approaches by exploiting certain extra temporal dependency assumptions: they allow significantly more correlated support change compared with batch methods. (2) The investigator is also developing novel provably accurate solutions for the online robust sparse-PCA problem, as well as several other related problems. (3) Finally, this project is helping to produce a well-trained and diverse future workforce. We expect our solutions to significantly transform the state-of-the-art in various big-data analytics applications, e.g., streaming video analytics, mobile video chats, autonomous vehicle or airplane navigation in foggy or rainy environments, anomalous or suspicious behavior detection from dynamic social network connectivity data.

在当今的大数据时代，我们周围产生了大量的流式大数据。其中大部分内容要么不存储，要么仅存储很短的时间，例如流媒体视频或更改社交网络连接。该项目开发了新颖的在线算法，用于从不完整或扭曲的数据中进行降维和结构信息恢复。这项研究对流式大数据结构恢复的理论和实践做出了一些贡献。 (1) 研究人员和她的团队正在开发可证明正确的在线算法，用于从欠采样、异常值损坏或其他高噪声流数据中进行稳健的结构（子空间或支持）跟踪。虽然解决这些问题的批处理方法已经得到了充分研究，但在线问题在很大程度上是开放的。我们的理论结果是鲁棒子空间跟踪（在线鲁棒PCA）和鲁棒支持跟踪问题的第一个正确性结果之一。在线算法非常有用，因为与大多数批处理技术相比，它们速度更快并且需要更少的存储空间。此外，我们的在线算法通过利用某些额外的时间依赖性假设消除了批处理方法的一个关键限制：与批处理方法相比，它们允许显着更多相关的支持变化。 (2) 研究人员还在为在线鲁棒稀疏 PCA 问题以及其他几个相关问题开发新的可证明准确的解决方案。 (3) 最后，该项目有助于培养一支训练有素、多元化的未来劳动力队伍。我们期望我们的解决方案能够显着改变各种大数据分析应用程序的最先进水平，例如流媒体视频分析、移动视频聊天、雾或多雨环境中的自动驾驶车辆或飞机导航、异常或可疑行为检测动态社交网络连接数据。