DC: Medium: Collaborative Research: Data Intensive Computing: Scalable, Social Data Analysis

DC：媒介：协作研究：数据密集型计算：可扩展、社交数据分析

• 批准号: 0964173
• 负责人: Jeffrey Heer
• 金额: $ 33.33万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2013-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0964173&HistoricalAwards=false
• 关键词: DC; Medium; Collaborative; Research; Data

Analysts in all areas of human knowledge, from science and engineeringto economics, social science and journalism are drowning in data. Theproliferation of digital information requires tools and techniques forexploring, analyzing and communicating data in a manner that scales asboth the data and the organizations analyzing it grow insize. Throughout the data life-cycle, sensemaking is often acollaborative process. As different analysts each contribute to dataacquisition, cleaning, analysis, and interpretation they contributecontextual knowledge that deepens understanding. Analysts may disagreeon how to interpret data, but then work together to reachconsensus. Many data sets are so large that thorough exploration by asingle person is unlikely. In short, social cognition plays a criticalrole in the process of scalable data analysis. New analysis tools thataddress human cognitive characteristics, social interaction and dataanalytics in an integrated fashion can improve our ability to turndata into knowledge.Scalable data analysis requires social interaction and thereforesocial context must be embedded in data analysis tools. The goals ofthis project are (1) to understand how social interaction and socialcontext can facilitate successful data analysis, (2) to develop modelsand tools for representing and annotating data transformations,visualizations, and social activity (e.g., textual and graphicalannotations, discussions, links, tags), and (3) to design and testvisual interfaces that leverage our tools to support collaborativeanalysis practices, including data entry, transformation,visualization, and interpretation. Central concerns include (a) afocus on enabling social interaction throughout the data life-cycleand (b) the use of scalable data transformation routines that canreturn results in a time frame concordant with interactive,exploratory data transformation and analysis.Further information on this project can be found at: http://vis.berkeley.edu/projects/scalable_social_data_analysis/

从科学和工程到经济学、社会科学和新闻学，人类知识各个领域的分析师都被数据淹没了。数字信息的激增需要工具和技术来探索、分析和交流数据，其方式随着数据和分析数据的组织规模的扩大而扩展。在整个数据生命周期中，意义建构通常是一个协作过程。由于不同的分析师各自致力于数据采集、清理、分析和解释，因此他们贡献了加深理解的背景知识。分析师可能对如何解释数据存在分歧，但随后会共同努力达成共识。许多数据集非常大，以至于一个人不可能进行彻底的探索。简而言之，社会认知在可扩展的数据分析过程中发挥着关键作用。以集成方式解决人类认知特征、社交互动和数据分析的新分析工具可以提高我们将数据转化为知识的能力。可扩展的数据分析需要社交互动，因此数据分析工具中必须嵌入社会背景。该项目的目标是（1）了解社交互动和社交背景如何促进成功的数据分析，（2）开发用于表示和注释数据转换、可视化和社交活动的模型和工具（例如文本和图形注释、讨论、链接） 、标签）和（3）设计和测试可视化界面，利用我们的工具支持协作分析实践，包括数据输入、转换、可视化和解释。核心问题包括（a）注重在整个数据生命周期中实现社交互动，以及（b）使用可扩展的数据转换例程，可以在与交互式、探索性数据转换和分析一致的时间范围内返回结果。有关该项目的更多信息可以位于：http://vis.berkeley.edu/projects/scalable_social_data_analysis/