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，我们周围产生了许多流媒体大数据。其中大多数不是仅存储或存储仅在短时间内存储，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，以降低维度的降低和结构信息从不完整或扭曲的数据中恢复。这项研究为从流媒体大数据流中的理论和结构恢复的实践做出了一些贡献。 （1）调查员和她的团队正在开发出可证明的在线结构（子空间或支持）跟踪的正确在线算法，该算法是从无效，离群折扣或其他高度嘈杂的流媒体数据中进行的。尽管解决了这些问题的批处理方法，但在线问题基本上是开放的。我们的理论结果是强大的子空间跟踪（在线鲁棒PCA）和强大的支持跟踪问题的第一个正确性结果之一。在线算法之所以有用，是因为它们比大多数批处理技术都更快，并且需要较少的存储空间。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来消除批处理方法的关键限制：与批处理方法相比，它们允许更大的相关支持更改。 （2）研究人员还正在为在线强大的稀疏PCA问题以及其他几个相关问题开发出新颖的准确解决方案。 （3）最后，该项目正在帮助培养训练有素且多样化的未来劳动力。我们希望我们的解决方案能够在各种大数据分析应用程序中重大改变最新的方法，例如，流媒体视频分析，移动视频聊天，自动驾驶汽车或飞机导航在有雾或雨水环境中，从动态社交网络连接数据中检测到异常或可疑行为。