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.

在今天的大数据时代，我们周围产生了大量的流大数据。其中大部分要么不存储，要么只存储很短的时间，例如，流媒体视频或改变社交网络连接。本项目主要研究基于流媒体数据的结构信息恢复和数据降维的在线算法，为流媒体数据结构信息恢复的理论和实践做出了贡献。(1)研究人员和她的团队正在开发可证明正确的在线算法，用于从采样不足，异常损坏或其他高噪声流数据中进行鲁棒的结构（子空间或支持）跟踪。虽然这些问题的批处理方法已经得到了很好的研究，在线问题在很大程度上是开放的。我们的理论结果是第一个正确的结果之间的鲁棒子空间跟踪（在线鲁棒PCA）和鲁棒支持跟踪问题。在线算法很有用，因为与大多数批处理技术相比，它们更快，需要更少的存储。此外，我们的在线算法消除了批处理方法的一个关键限制，利用某些额外的时间依赖性假设：它们允许显着更多的相关支持变化相比，批处理方法。(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，该项目有助于培养训练有素和多样化的未来劳动力。我们希望我们的解决方案能够显著改变各种大数据分析应用的最新技术，例如，流视频分析、移动的视频聊天、雾天或雨天环境中的自主车辆或飞机导航、来自动态社交网络连接数据的异常或可疑行为检测。