CAREER: A Collaborative Adaptive Data Sharing Platform

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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.