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

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，所有人类知识领域的分析师都淹没在数据中。数字信息的激增需要工具和技术来探索、分析和交流数据，以一种随着数据和分析数据的组织规模的扩大而扩展的方式。在整个数据生命周期中，意义生成通常是一个协作过程。由于不同的分析师每个人都对数据采集、清理、分析和解释做出了贡献，他们贡献了加深理解的上下文知识。分析师可能在如何解释数据方面存在分歧，但随后会共同努力达成共识。许多数据集如此之大，单个人不太可能对其进行彻底的探索。简而言之，社会认知在可扩展数据分析过程中起着至关重要的作用。将人类认知特征、社会互动和数据分析结合起来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会互动，因此社会背景必须嵌入到数据分析工具中。这个项目的目标是(1)了解社会互动和社会背景如何促进成功的数据分析，(2)开发模型和工具来表示和注释数据转换、可视化和社会活动（例如，文本和图形注释、讨论、链接、标签），以及(3)设计和测试可视化界面，利用我们的工具来支持协作分析实践，包括数据输入、转换、可视化、和解释。核心问题包括(a)关注在整个数据生命周期中实现社会交互，以及(b)使用可扩展的数据转换例程，该例程可以在与交互式、探索性数据转换和分析相一致的时间框架内返回结果。关于这个项目的更多信息可以在http://vis.berkeley.edu/projects/scalable_social_data_analysis/上找到