CAREER: A Collaborative Adaptive Data Sharing Platform

职业：协作自适应数据共享平台

• 批准号: 1216007
• 负责人: Evangelos Christidis
• 金额: $ 51.12万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-31 至 2017-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1216007&HistoricalAwards=false
• 关键词: CAREER; Collaborative; Adaptive; Data; Sharing

CAREER: A Collaborative Adaptive Data Sharing PlatformThe increased popularity of domain social networking and blogs is creating a huge amount of shared data. Properly annotating this data would allow its effective searching and analysis. Consider as a specific motivating application a disaster mitigation collaboration network for businesses. Using keyword search to find open child care locations after a hurricane would require sifting through hundreds of shared documents. Current data sharing platforms provide little help to the users to effectively and effortlessly annotate their data in a way that will benefit the information demand of other users. The long term goal of this project is to leverage the collective knowledge of communities to increase the utility of shared information. The objective of this project is to create the knowledge and techniques to allow the users of an application domain to effectively and effortlessly annotate, share and query data, by exploiting the past user interactions -- i.e., data annotations, query workload and user query relevance feedback. A key novelty of the proposed Collaborative Adaptive Data Sharing Platform (CADS) is that the past user interactions are leveraged to effectively annotate the data at insertion-time. The intellectual merit of this project is the facilitation of effective annotation, matching and querying of shared data by leveraging the user interactions at insertion and query time. The algorithms for the transformative concept of adaptive insertion form, which will suggest the best attributes, values and matchings to annotate the to-be-inserted data, will estimate the information value and confidence of a candidate annotation and dependencies analysis on the query workload. The adaptive query form algorithms which will guide the user in formulating effective queries, will exploit past user interactions to estimate the user?s affinity to a condition. All algorithms will be evaluated with real users and datasets.This project is expected to have the following broader impacts: (a) Promote participation of FIU (one of the largest Hispanic institutes in the country) minority students in the research process. This is expected to attract more minority students to pursue MS or Ph.D. in computer science, which is hindered by the lack of exposure to academic opportunities. (b) Facilitate effective collaboration and information sharing among the members of communities -- e.g. disaster management, scientific, news.

职业：协作自适应数据共享平台领域社交网络和博客的日益普及正在创建大量的共享数据。适当地注释这些数据将允许其有效的搜索和分析。考虑作为一个具体的激励应用程序的企业减灾协作网络。使用关键字搜索在飓风后找到开放的儿童保育地点需要筛选数百个共享文档。当前的数据共享平台几乎没有帮助用户以有益于其他用户的信息需求的方式有效且毫不费力地注释他们的数据。该项目的长期目标是利用社区的集体知识来提高共享信息的效用。该项目的目标是创建知识和技术，以允许应用程序域的用户有效地和毫不费力地注释，共享和查询数据，通过利用过去的用户交互-即，数据注释、查询工作量和用户查询相关性反馈。所提出的协作自适应数据共享平台（CADS）的一个关键新奇在于，利用过去的用户交互来在插入时有效地注释数据。该项目的智力价值是通过利用插入和查询时的用户交互来促进对共享数据的有效注释、匹配和查询。自适应插入形式的转换概念的算法将建议最佳属性、值和匹配来注释待插入的数据，将估计候选注释的信息值和置信度以及对查询工作负载的依赖性分析。自适应查询形式的算法，这将引导用户在制定有效的查询，将利用过去的用户交互来估计用户？对一个条件的亲和力。将用真实的用户和数据集对所有算法进行评估，预计该项目将产生以下更广泛的影响：（a）促进金融情报机构（该国最大的西班牙裔学院之一）少数民族学生参与研究进程。预计这将吸引更多的少数民族学生攻读硕士或博士学位。在计算机科学，这是阻碍了缺乏接触学术机会。(b)促进社区成员之间的有效协作和信息共享-例如灾害管理、科学、新闻。