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

本项目开发了一个新的并行计算平台，即可视化计算数据库，以促进需要大规模可视化数据分析的应用程序的开发。所开发的系统结合了传统的关系数据库管理系统的思想（更容易和强大的组织和管理可视化数据集）与现代图形编程抽象有效地操纵像素数据。该项目实现了一个可视化计算数据库的原型，将其作为开源项目发布给社区，并将该系统大规模部署为Google Cloud Platform上的科学家和研究人员提供服务。有强有力的证据表明，在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以及科学数据分析的领域中，下一代视觉和体验计算（VEC）应用程序的基本要求将是对大型视觉数据库（图像，视频，RGBD等）的有效分析和挖掘。缩放视觉数据分析应用程序来操作集合，例如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PB图像，由于视觉数据表示的大小以及算法理解和操纵大型图像数据集的计算费用，提出了重大的计算机科学挑战。从头开始开发高效的超级计算规模应用程序的困难抑制了该领域探索高级数据驱动VEC应用程序的能力。该项目的一个核心方面是设计一种新的可视化数据查询语言，该语言将高性能功能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，提供在数据库（靠近数据存储）中高效执行复杂图像/视频分析操作序列的能力。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的紧密集成（例如，大规模机器学习、图像配准/对齐和3D重建），关键的设计挑战是使数据库操作的结果易于被非关系的超级计算规模的计算访问。总而言之，该项目解决了基本的系统设计问题，例如：对于未来的视觉数据分析任务，什么是良好的视觉查询语言？如何在大规模的吞吐量硬件上有效地实施关键操作？评估大规模可视化数据分析系统的适当基准是什么？网址：http://graphics.cs.cmu.edu/projects/visualdb

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