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

今天，个人可获得的信息量是巨大的，而且正在迅速增加。人们不断地理解这个世界：科学家在一个不熟悉的领域学习文献；分析师发现计算机网络中的异常活动；患者理解他们的症状。从用户的角度来看，主要问题与存储、计算能力或大规模数据处理无关。它更多的是关于如何最好地放大他或她有限的认知力，通过“自然”的交互探索来理解一个庞大的数据语料库。该项目将承担信息丰富的亿级网络数据集的人机交互探索的挑战。这些包括在线社交网络(谁连接到谁)，在线拍卖(谁在买什么)，以及对通信模式和网络流量的情报分析。它将把人机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。具体地说，它将设计和开发一个原型系统，在这个系统中，用户将逐渐建立对数十亿规模的网络数据集的理解。这项研究可能会从根本上改变人们在许多领域对数据的理解方式，如科学文献、网络安全和消费者决策。这个项目将结合多种新的想法，组织成四个相互关联的研究推力：(1)使用最小描述长度原则(MDL)、KL发散和组合差异的自适应局部探索。(2)基于算法隐形传态工具的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时间演变网络模式的机制。(3)可扩展的数据模型和算法，以支持先前推力的交互需求。拟议的工具将通过Egonet Edge分区和分布式稀疏和持久的多维排序映射来处理存储布局。(4)研究人员将在关键领域持续进行多阶段评估，在整个开发过程中与用户合作。这些将包括通过面对面的用户研究、虚拟实验室研究和纵向现场试验进行迭代界面开发。有关更多信息，请参阅项目网站at:http://poloclub.gatech.edu/human-computer-telediscovery/

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/