BIGDATA: Small: DCM: DA: Collaborative Research: SMASH -- Scalable Multimedia content AnalysiS in a High-level language

大数据： 小： DCM： DA： 协作研究： SMASH - 使用高级语言进行可扩展多媒体内容分析

• 批准号: 1251276
• 负责人: Gerald Friedland
• 金额: $ 40.4万
• 依托单位: International Computer Science Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-15 至 2019-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1251276&HistoricalAwards=false
• 关键词: BIGDATA; Small; DCM; DA; Collaborative

This big data project develops tools to support researchers and developers in the task of prototyping multimedia content analysis algorithms in a large scale. Typically, scientists and engineers prefer to use high-level programming languages such as Python or MATLAB to conduct experiments, as they allow for a quick implementation of a novel idea. Experiments on big data, however, are often computationally-intensive and therefore must eventually be recoded into a low-level language by expert programmers in order to achieve sufficient performance, creating a gap between productivity and performance. In addition, multiple strategies may exist for mapping a problem onto parallel hardware depending on the input data size and the hardware parameters, further exacerbating the problem. Using the application area of multimedia content analysis as an example (an area with one of the largest and the fastest growing amounts of data due to the steady upload of consumer produced videos), this project performs research on a pattern-oriented, application-specific specialization framework that uses a tiered approach to parallel programming. The ultimate aim is to provide the scalability of diverse parallel processing at the productivity level of high-level languages.Social media videos are increasingly being used for scientific research, as they allow us to observe and model many phenomena studied, for example, in social sciences, economics, meteorology and medicine. More scalable content analysis impacts any field that uses social media videos. Moreover, social media videos are an everyday part of many people's lives. Making multimedia content analysis more scalable allows for better algorithms to be developed by more students and researchers, and therefore impacts many people's lives. The framework is made available on the project website (http://smash.icsi.berkeley.edu).

该大数据项目开发工具来支持研究人员和开发人员完成大规模多媒体内容分析算法原型设计的任务。通常，科学家和工程师更喜欢使用 Python 或 MATLAB 等高级编程语言来进行实验，因为它们可以快速实现新想法。然而，大数据实验通常是计算密集型的，因此最终必须由专家程序员重新编码为低级语言才能获得足够的性能，从而在生产力和性能之间造成了差距。此外，根据输入数据大小和硬件参数，可能存在多种策略将问题映射到并行硬件上，这进一步加剧了问题。以多媒体内容分析的应用领域为例（由于消费者制作的视频的稳定上传，该领域是数据量最大且增长最快的领域之一），该项目研究了面向模式的、针对特定应用的使用分层方法进行并行编程的专业化框架。最终目标是在高级语言的生产力水平上提供多种并行处理的可扩展性。社交媒体视频越来越多地用于科学研究，因为它们使我们能够观察和建模许多研究的现象，例如，在社交媒体中科学、经济学、气象学和医学。更具可扩展性的内容分析会影响使用社交媒体视频的任何领域。此外，社交媒体视频是许多人日常生活的一部分。使多媒体内容分析更具可扩展性，可以让更多的学生和研究人员开发出更好的算法，从而影响许多人的生活。该框架可在项目网站 (http://smash.icsi.berkeley.edu) 上获取。