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

MRI：开发：用于大容量数据流的分布式可视化分析沙箱

• 批准号: 1429526
• 负责人: Raju Gottumukkala
• 金额: $ 50万
• 依托单位: University of Louisiana at Lafayette
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1429526&HistoricalAwards=false
• 关键词: MRI; Development; Distributed; Visual; Analytics

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.

该项目设计和开发了一种可以支持大容量，高速数据流的可视化分析的仪器，旨在为研究人员提供一个易于使用的软件接口，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系统的紧密互连，以处理实时内存流系统处理和复杂分析，沿着专用可视化处理。正在开发的仪器：-提供定制的计算系统设计，预计将提供比商业智能解决方案的商业硬件供应商提供的现有系统高出一个数量级的性能。为决策者建立高度可扩展的大数据平台或处理物联网产生的大数据，以及代表运输路线，疾病爆发路径，社交社区网络，金融交易和推荐图的动态社交网络的知识和理解。支持视觉和决策信息学中心（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特定研究项目，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通过远程内存访问（RDMA）紧密互连的处理器功能、低延迟固态硬盘（SSD）和Infiniband互连，以支持分布式内存数据流预处理和分析。深度分析节点包括可以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，以支持海量数据集和高级可视化场所的可视化。支持知识发现和决策需要一个强大的计算基础设施，没有它，从传感器，移动的电话，社交网络的网络构建的大型复杂的动态图的可视化分析无法实现。该仪器支持许多领域的应用，例如：灾害响应，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Lousiana光网络计划（LONI）和Internet 2的连接促进了与全国其他校区和美国社区研究人员的合作伙伴关系。该仪器将为学生提供服务，并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，从而提高了STEM领域的参与度。此外，该工具使许多研究人员和教育工作者的生产力。

项目成果

VAStream: A Visual Analytics System for Fast Data Streams

• DOI: 10.1145/3332186.3332256
• 发表时间: 2019-07
• 期刊: Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)
• 作者: Satya Katragadda;Raju N. Gottumukkala;Siva R. Venna;Nicholas G. Lipari;Shailendra Gaikwad;Murali K. Pusala;Jian Chen;C. Borst;Vijay A. Raghavan;M. Bayoumi

Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units

• DOI: 10.3390/en12214055
• 发表时间: 2019-10
• 期刊: Energies
• 影响因子: 3.2
• 作者: Jessica Wojtkiewicz;Matin Hosseini;Raju N. Gottumukkala;T. Chambers

ClustCrypt: Privacy-Preserving Clustering of Unstructured Big Data in the Cloud

• DOI: 10.1109/hpcc/smartcity/dss.2019.00093
• 发表时间: 2019-08
• 期刊: 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
• 作者: S. Zobaed;Sahan Ahmad;Raju N. Gottumukkala;M. Salehi

Distributed Real Time Link Prediction on Graph Streams

图流上的分布式实时链路预测

• DOI: 10.1109/bigdata.2018.8621934
• 发表时间: 2018
• 期刊: 2018 IEEE International Conference on Big Data (Big Data
• 作者: Katragadda, Satya;Gottumukkala, Raju;Pusala, Murali;Raghavan, Vijay;Wojtkiewicz, Jessica
• 通讯作者: Wojtkiewicz, Jessica

Direct Normal Irradiance Forecasting Using Multivariate Gated Recurrent Units

• DOI: 10.3390/en13153914
• 发表时间: 2020-07
• 期刊: Energies
• 影响因子: 3.2
• 作者: Majid Hosseini;Satya Katragadda;Jessica Wojtkiewicz;Raju N. Gottumukkala;A. Maida;T. Chambers