CAREER: A Collaborative Adaptive Data Sharing Platform

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造大量共享数据。适当地注释这些数据将允许对其进行有效的搜索和分析。考虑将企业减灾协作网络作为一个具体的激励应用程序。飓风过后，使用关键字搜索来寻找开放的托儿所地点需要筛选数百份共享文件。目前的数据共享平台对用户有效、轻松地对数据进行注释以满足其他用户的信息需求提供的帮助很少。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。该项目的目标是创建知识和技术，通过利用过去的用户交互（即数据注释、查询工作负载和用户查询相关性反馈），允许应用程序域的用户有效且毫不费力地注释、共享和查询数据。所提出的协作自适应数据共享平台（CADS）的一个关键新颖之处在于利用过去的用户交互在插入时有效地注释数据。这个项目的智力优势在于，通过在插入和查询时利用用户交互，简化了共享数据的有效注释、匹配和查询。自适应插入表单的转换概念的算法将建议最佳的属性、值和匹配来注释要插入的数据，将估计候选注释的信息价值和置信度，以及对查询工作负载的依赖分析。自适应查询表单算法将指导用户制定有效的查询，将利用过去的用户交互来估计用户？对一个条件的亲和力。所有算法都将使用真实用户和数据集进行评估。预计该项目将产生以下更广泛的影响：(a)促进少数民族学生参与研究过程的FIU（该国最大的西班牙裔研究所之一）。预计这将吸引更多少数族裔学生攻读计算机科学的硕士或博士学位，这一学科受到缺乏学术机会的阻碍。(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。

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.