III: Medium: Collaborative Research: Human-Computer Graph Exploration and Tele-Discovery

III：媒介：协作研究：人机图探索与远程发现

• 批准号: 1563971
• 负责人: James Abello
• 金额: $ 60万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1563971&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Human

The amount of information available to individuals today is enormous and rapidly increasing. People are constantly making sense of the world: scientists learning the literature in an unfamiliar field; analysts spotting abnormal activities in computer networks; and patients understanding their symptoms. From a user's perspective, the main issue is not about storage, or computing power, or large scale data processing. It is more about how to best amplify his or her limited cognition power to make sense of a large data corpus via "natural" interactive exploration. This project will undertake the challenge of computer-human interactive exploration of information-rich billion-scale network datasets. These include online social networks (who is connected to whom), online auctions (who is buying what), and intelligence analysis of communication patterns and network traffic. It will blend computer-human interaction principles and decomposable visualizations with new scalable exploration techniques that are driven by information-theoretic measures. Specifically, it will design and develop a prototype system, in which users will gradually build up an understanding of billion-scale network datasets. This research could fundamentally change how people make sense of data in many domains like scientific literature, cybersecurity, and consumer decision making. The findings could increase education effectiveness, rate of scientific discovery, and enable more literate, knowledgeable, and intelligent citizens.This project will combine multiple novel ideas synergistically, organized into four inter-related research thrusts: (1) Adaptive Local Exploration using Minimum Description Length principles (MDL), KL divergence and Combinatorial Discrepancy. (2) Pattern Tele-Discovery & Global Summarization via algorithmic teleportation tools. These will include mechanisms for querying, discovering, linking, and visualizing multi-attributed time-evolving network patterns. (3) Scalable Data Models & Algorithms to support the interactivity demands of the previous thrusts. The proposed tools will address storage layouts via Egonet Edge Partitions and distributed sparse and persistent multidimensional sorted maps. (4) The researchers will continually conduct multi-stage evaluations in key domains, working with users throughout the entire development process. These will include iterative interface development via in-person user studies, virtual lab studies, and longitudinal field trials. For further information see the project web site at:http://poloclub.gatech.edu/human-computer-telediscovery/

如今，个人可以获得的信息量非常巨大，而且还在迅速增加。人们不断地理解世界：科学家在陌生领域学习文献；分析师发现计算机网络中的异常活动；以及患者了解自己的症状。从用户的角度来看，主要问题不是存储、计算能力或大规模数据处理。更多的是如何最好地放大他或她有限的认知能力，通过“自然”的交互式探索来理解大数据语料库。该项目将承担对信息丰富的数十亿级网络数据集进行人机交互探索的挑战。其中包括在线社交网络（谁与谁连接）、在线拍卖（谁在购买什么）以及通信模式和网络流量的情报分析。它将把计算机与人的交互原理和可分解的可视化与由信息论措施驱动的新的可扩展探索技术相结合。具体来说，它将设计和开发一个原型系统，用户将在其中逐步建立对数十亿规模网络数据集的理解。这项研究可以从根本上改变人们在科学文献、网络安全和消费者决策等许多领域理解数据的方式。这些发现可以提高教育效率、科学发现率，并培养更多有文化、知识渊博、聪明的公民。该项目将协同地结合多种新颖的想法，分为四个相互关联的研究主旨：（1）使用最小描述长度原则（MDL）、KL散度和组合差异的自适应局部探索。 (2) 通过算法隐形传输工具进行模式远程发现和全局总结。这些将包括用于查询、发现、链接和可视化多属性随时间演化的网络模式的机制。 (3) 可扩展的数据模型和算法，以支持先前主旨的交互性需求。所提出的工具将通过 Egonet Edge Partitions 和分布式稀疏和持久多维排序映射来解决存储布局问题。 （4）研究人员将持续在关键领域进行多阶段评估，并在整个开发过程中与用户合作。这些将包括通过现场用户研究、虚拟实验室研究和纵向现场试验进行迭代界面开发。欲了解更多信息，请参阅该项目网站：http://poloclub.gatech.edu/ human-computer-telediscovery/