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: 0963922
• 负责人: Maneesh Agrawala
• 金额: $ 66.67万
• 依托单位: University of California-Berkeley
• 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 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0963922&HistoricalAwards=false
• 关键词: DC; Medium; Collaborative; Research; Data; 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，社会科学和新闻业的所有知识领域的分析师都淹没在数据中。数字信息的扩散需要以探索，分析和传达数据的方式以缩放数据和分析其分析的组织的方式进行探索，分析和传达数据。在整个数据寿命周期中，感官通常是实验室的过程。随着不同的分析师为数据促进，清洁，分析和解释做出贡献，他们贡献了加深理解的视野知识。分析师可能会不同意如何解释数据，但随后共同努力以进行接触。许多数据集是如此之大，以至于不太可能对Asingle人进行彻底的探索。简而言之，社会认知在可扩展数据分析的过程中起着至关重要的作用。新的分析工具可以以综合的方式来补充人类的认知特征，社会互动和数据分析，可以提高我们将turndata转化为知识的能力。可估计的数据分析需要社交互动，因此必须将社会互动嵌入到数据分析工具中。该项目的目标是（1）了解社交互动和社交界面如何促进成功的数据分析，（2）开发模型和工具，用于表示和注释数据转换，可视化和社交活动（例如，文本和图形和图形词和图形，讨论，讨论，链接，链接，标签），以及将我们的工具置于设计和测试的练习中，以支持我们的工具，以支持我们的工具，以促进工具，以实现范围的工具。转换，可视化和解释。 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/