MRI: Development: A Distributed Visual Analytics Sandbox for High Volume Data Streams

该项目旨在设计和开发一种能够支持大容量、高速数据流可视化分析的仪器，旨在为研究人员提供一个易于使用的软件界面，以开发需要流处理、深度分析和可视化功能相结合的可视化分析应用程序。该仪器提供计算能力和紧密的系统互连，以处理实时内存流系统处理和复杂分析，以及专用的可视化处理。正在开发的仪器：-提供定制的计算系统设计，期望提供比商业智能解决方案硬件供应商提供的现有系统高一个数量级的性能。-提升知识和理解，为决策者构建高可扩展性的大数据平台，或处理来自物联网的大数据，以及代表交通路线、疾病爆发路径、社会社区网络、金融交易和推荐图的动态社交网络。-支持视觉与决策信息学中心（CVDI）的数据挖掘和分析需求，以及开发传感器数据流视觉技术的具体研究项目，并建立在高级计算研究中心（CACS）互联网计算实验室（LINC）建立的实验性云基础设施之上。实时流处理和分析系统包括具有远程内存访问（RDMA）功能的紧密互连处理器，具有Infiniband互连的低延迟固态硬盘（SSD），以支持分布式内存数据流预处理和分析。深度分析节点包括能够处理高效批处理的数据节点和具有高端显卡的可视化处理节点，支持海量数据集的可视化和高级可视化场所。支持知识发现和决策需要强大的计算基础设施；没有它，就无法实现对由传感器、手机、社交网络构成的大型复杂动态图形的可视化分析。该工具支持许多领域的应用，例如：灾害应对、公共安全、公共卫生、网络安全、电子商务和金融部门。基于GENI（全球网络创新环境）的连接路易斯安那州的光网络倡议（LONI）和Internet2促进了与全国其他校园和美国Ignite社区的研究人员的合作关系。该仪器将被学生用于大数据研究和教育。利用路易斯·斯托克斯-洛杉矶少数民族参与联盟（LS-LAMP），该项目还促进了代表性不足的学生的参与，从而增加了STEM领域的参与度。此外，该工具还为许多研究人员和教育工作者提供了生产力。

This project, designing and developing an instrument that can support visual analytics on high volume, high velocity data streams, aims to offer an easy to use software interface for researchers to develop visual analytics applications that need a combination of stream processing, deep analytics, and visualization capabilities. The instrument provides the computational capacity and tight interconnection of systems to handle both real-time in-memory stream system processing and complex analytics, along with dedicated visualization processing. The instrument under development:- Offers a customized computational system design expected to offer up to an order of magnitude performance over existing systems offered by commercial hardware vendors of business intelligent solutions.- Advances knowledge and understanding in building highly-scalable big data platforms for decision makers or deals with big data generated from internet of things, and dynamic social networks that represent transportation routes, paths of disease outbreaks, social community networks, financial transactions, and recommendation graphs.- Supports data mining and analytical needs of the Center for Visual and Decision Informatics (CVDI) and specific research projects to develop visual techniques on sensor data streams, and builds upon the experimental cloud infrastructure established in the Center for Advanced Computing Studies (CACS) Lab for InterNet Computing (LINC).The real-time stream processing and analytics system comprises tightly interconnected processors with Remote Memory Access (RDMA) capabilities, low-latency Solid State Drive (SSD) with Infiniband Interconnect to support distributed in-memory data stream pre-processing, and analytics. The deep analytics nodes comprise data nodes that can handle efficient batch processing and the visualization processing node with high-end graphics cards to support visualization of massive data sets and advanced visualization venues. Supporting knowledge discovery and decision making requires a powerful computational infrastructure; without it, visual analytics on large complex dynamic graphs constructed from networks of sensors, mobile phones, social networks cannot be realized. The instrument supports applications in many areas such as: disaster response, public safety, public health, cyber security, ecommerce and financial sectors. GENI (Global Environment for Network Innovations)-based connectivity to Lousiana's Optical Network Initiative (LONI) and Internet2 facilitates partnerships with researchers across other campuses nationwide and the US Ignite Community. The instrument will serve and be used by students for big data research and education. Utilizing the Louis Stokes-LA Alliance for Minority Participation (LS-LAMP), the project also facilitates participation of underrepresented students, thus increasing more participation in STEM areas. Furthermore, the instrument enables productivity for many researchers and educators.