VEC: Small: Collaborative Research: The Visual Computing Database: A Platform for Visual Data Processing and Analysis at Internet Scale

VEC：小型：协作研究：视觉计算数据库：互联网规模的视觉数据处理和分析平台

• 批准号: 1539069
• 负责人: Kayvon Fatahalian
• 金额: $ 11.02万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1539069&HistoricalAwards=false
• 关键词: VEC; Small; Collaborative; Research; Visual

This project develops a new parallel computing platform, namely Visual Computing Database, that facilitates the development of applications that require visual data analysis at massive scale. The developed system combines ideas from traditional relational database management systems (to more easily and powerfully organize and manage visual data collections) with modern graphics programming abstractions for efficiently manipulating pixel data. This project implements a prototype of the visual computing database, release it as an open source project to the community, and deploys the system at scale as a service to scientists and researchers on the Google Cloud Platform. There is strong evidence that in domains ranging from personal digital assistants that interpret one's surroundings, to management of critical infrastructure in smart cities, and to scientific data analysis, a fundamental requirement of the next generation of visual and experiential computing (VEC) applications will be the efficient analysis and mining of large repositories of visual data (images, videos, RGBD, etc.). Scaling visual data analysis applications to operate on collections such as the photos and videos on Facebook and YouTube, the traffic cameras in a city, or petabytes of images in a digital sky survey, presents significant computer science challenges due to the size of visual data representations and the computational expense of algorithms understanding and manipulating large image datasets. The difficulty of developing efficient, supercomputing scale applications from scratch inhibits the field's ability to explore advanced data-driven VEC applications. A central aspect of the project is the design of a new visual data query language that integrates concepts from high performance, functional image processing languages with relational operators and spatial and temporal predicates, providing the ability to execute sequences of complex image/video analysis operations with high efficiency in the database (near the data store). Since visual analysis workloads involve tight integration of data retrieval operations and processing of the result sets (e.g., largescale machine learning, image registration/alignment, and 3D reconstruction), a key design challenge is making the results of database operations easily accessible to non-relational, supercomputing scale computations. All together the project addresses fundamental systems design questions such as: what is a good visual query language for future visual data analysis tasks? How can key operations be implemented efficiently on throughput hardware at scale? What are the appropriate benchmarks for evaluating visual data analysis systems at scale?URL: http://graphics.cs.cmu.edu/projects/visualdb

该项目开发了一个新的并行计算平台，即可视化计算数据库，以方便开发需要大规模可视化数据分析的应用程序。开发的系统结合了传统关系数据库管理系统的思想(更容易和更强大地组织和管理可视数据集合)与现代图形编程抽象，以有效地操作像素数据。该项目实现了视觉计算数据库的原型，将其作为开源项目发布给社区，并将该系统作为服务大规模部署到Google Cloud平台上的科学家和研究人员。有强有力的证据表明，从解释周围环境的个人数字助理，到智能城市中关键基础设施的管理，再到科学数据分析，下一代视觉和体验计算(VEC)应用的基本要求将是对大型视觉数据(图像、视频、RGBD等)的高效分析和挖掘。将可视数据分析应用程序扩展到对Facebook和YouTube上的照片和视频、城市中的交通摄像头或数字天空调查中的数PB图像等集合进行操作，由于可视数据表示的大小以及理解和操作大型图像数据集的算法的计算成本，带来了重大的计算机科学挑战。从零开始开发高效、超级计算规模的应用程序的难度限制了该领域探索高级数据驱动的VEC应用程序的能力。该项目的一个中心方面是设计一种新的可视数据查询语言，该语言将高性能、功能性图像处理语言的概念与关系运算符和空间和时间谓词相结合，能够在数据库(数据存储附近)中高效地执行复杂的图像/视频分析操作序列。由于可视化分析工作负载涉及数据检索操作和结果集处理(例如，大规模机器学习、图像配准/对齐和3D重建)的紧密集成，因此一个关键的设计挑战是使非关系型、超级计算规模的计算能够容易地访问数据库操作的结果。总而言之，该项目解决了基本的系统设计问题，例如：对于未来的可视化数据分析任务，什么是良好的可视化查询语言？如何在规模化吞吐量硬件上高效实施关键操作？评估大规模视觉数据分析系统的合适基准是什么？网址：http://graphics.cs.cmu.edu/projects/visualdb