
NSF Org: |
IIS Division of Information & Intelligent Systems |
Recipient: |
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Initial Amendment Date: | December 13, 2011 |
Latest Amendment Date: | June 23, 2016 |
Award Number: | 1216007 |
Award Instrument: | Continuing Grant |
Program Manager: |
Sylvia Spengler
sspengle@nsf.gov (703)292-7347 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | October 31, 2011 |
End Date: | March 31, 2017 (Estimated) |
Total Intended Award Amount: | $511,242.00 |
Total Awarded Amount to Date: | $589,242.00 |
Funds Obligated to Date: |
FY 2011 = $108,009.00 FY 2012 = $166,408.00 FY 2013 = $115,271.00 FY 2014 = $120,750.00 FY 2015 = $16,000.00 FY 2016 = $16,000.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
200 UNIVERSTY OFC BUILDING RIVERSIDE CA US 92521-0001 (951)827-5535 |
Sponsor Congressional District: |
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Primary Place of Performance: |
200 University Office Building Riverside CA US 92521-0001 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | Info Integration & Informatics |
Primary Program Source: |
01001112DB NSF RESEARCH & RELATED ACTIVIT 01001213DB NSF RESEARCH & RELATED ACTIVIT 01001314DB NSF RESEARCH & RELATED ACTIVIT 01001415DB NSF RESEARCH & RELATED ACTIVIT 01001516DB NSF RESEARCH & RELATED ACTIVIT 01001617DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
CAREER: A Collaborative Adaptive Data Sharing Platform
The 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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project created solutions to the problem of annotating and searching text based documents, such as medical publications or social network posts. Algorithms were developed to automatically generate annotations, for example, annotate a laptop review with “hard disk size = 1 TB”, which allows users to easier sift through large numbers of documents by specifying appropriate conditions.
The intellectual merit of this project also included: (a) novel techniques to interactively search collections of objects, such as products, by generating refinement suggestions that minimize the expected effort of the user; (b) methods to select a subset of the most important social posts to display to a user to avoid the information overload problem; (c) methods to account for the creation time of documents when ranking the results of a query; (d) discovering which keyword queries are difficult to be answered effectively in order to advise the user to reformulate them.
The broader impacts consisted of: (a) high school outreach activities, where presentations were given to local high-schools on the topic of computer science college education and careers; (b) Information Retrieval and Big Data courses were created at the University of California, Riverside; (c) improved methods to search biomedical data were created that may potentially help biomedical scientists be more productive; (d) improved search and summarization techniques on social networks may improve users’ experience; (e) about twenty undergraduate students were involved in research projects, and about half of them continued to graduate school.
Last Modified: 04/09/2017
Modified by: Evangelos Christidis
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