CAREER: A Collaborative Adaptive Data Sharing Platform

职业：协作自适应数据共享平台

• 批准号: 0952347
• 负责人: Evangelos Christidis
• 金额: $ 52.59万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2012-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0952347&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：一个协作的自适应数据共享平台，域社交网络和博客的普及正在创建大量共享数据。正确注释这些数据将允许其有效的搜索和分析。将作为一种特定的激励应用程序视为企业的灾难缓解协作网络。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件。当前的数据共享平台几乎没有帮助用户有效，毫不费力地注释其数据，从而使其他用户的信息需求受益。该项目的长期目标是利用社区的集体知识来增加共享信息的效用。该项目的目的是创建知识和技术，以允许应用程序领域的用户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）来有效而轻松地注释，共享和查询数据。拟议的协作自适应数据共享平台（CADS）的主要新颖性是，将过去的用户交互借给了在插入时间有效注释数据。该项目的智力优点是通过利用插入和查询时间的用户交互来促进有效注释，匹配和查询共享数据。自适应插入形式的变革性概念的算法，该算法将提出注释的最佳属性，价值和匹配，以估算候选人注释和依赖关系分析的信息价值和信心。自适应查询形式算法将指导用户制定有效查询，将利用过去的用户交互以估算用户的亲和力对条件。所有算法将与真实用户和数据集进行评估。该项目有望产生以下更广泛的影响：（a）促进FIU（该国最大的西班牙裔研究所之一）少数群体学生参与研究过程。预计这将吸引更多的少数学生攻读MS或博士学位。在计算机科学中，由于缺乏学术机会而受到阻碍。 （b）促进社区成员之间有效的合作和信息共享，例如灾难管理，科学，新闻。