Award Abstract # 1216007
CAREER: A Collaborative Adaptive Data Sharing Platform

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: REGENTS OF THE UNIVERSITY OF CALIFORNIA AT RIVERSIDE
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 2010 = $46,804.00
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:
  • Evangelos Christidis (Principal Investigator)
    evangelos.christidis@ucr.edu
Recipient Sponsored Research Office: University of California-Riverside
200 UNIVERSTY OFC BUILDING
RIVERSIDE
CA  US  92521-0001
(951)827-5535
Sponsor Congressional District: 39
Primary Place of Performance: University of California-Riverside
200 University Office Building
Riverside
CA  US  92521-0001
Primary Place of Performance
Congressional District:
39
Unique Entity Identifier (UEI): MR5QC5FCAVH5
Parent UEI:
NSF Program(s): Info Integration & Informatics
Primary Program Source: 01001011DB NSF RESEARCH & RELATED ACTIVIT
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): 1045, 7364, 9251
Program Element Code(s): 736400
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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Azade Nazi, Saravanan Thirumuruganathan, Vagelis Hristidis, Nan Zhang and Gautam Das "Answering Complex Queries in an Online Community Network" AAAI Conference on Weblogs and Social Media (ICWSM) , v.Poster , 2015
Eduardo J. Ruiz, Vagelis Hristidis, Panagiotis G. Ipeirotis. "Facilitating Document Annotation using Content and Querying Value" IEEE Transactions on Knowledge and Data Engineering (TKDE) , v.26 , 2014
Ellen Leslie Brown, Nicole Ruggiano, Timothy F. Page, Lisa Roberts, Vagelis Hristidis, Karen L. Whiteman, Joana Castro. "CareHeroes Web and Android Apps for Dementia Caregivers: A Feasibility Study" Research in Gerontological Nursing. , 2016 10.3928/19404921-20160229-02
Matthew T. Wiley, Ryan L. Rivas and Vagelis Hristidis "Provider attributes correlation analysis to their referral frequency and awards" BMC Health Services Research , v.16 , 2016
Matthew Wiley, Canghong Jin, Vagelis Hristidis, Kevin M Esterling "Pharmaceutical Drugs Chatter on Online Social Networks" Elsevier Journal of Biomedical Informatics , v.49 , 2014 , p.245
Moloud Shahbazi, Joseph R. Barr, Vagelis Hristidis and Nani Narayanan Srinivasan "Estimation of the Investability of Real Estate Properties Through Text Analysis." IEEE International Conference on Semantic Computing (ICSC) , 2016
Moloud Shahbazi, Matthew Wiley and Vagelis Hristidis "IRanker: Query-Specific Ranking of Reviewed Items" IEEE International Conference on Data Engineering (ICDE) 2017, short paper (4 pages) , 2017
Nhat Le, Vagelis Hristidis and Neal Young "Ontology- and Sentiment-aware Review Summarization" IEEE International Conference on Data Engineering (ICDE) 2017 (short paper, 4 pages) , 2017
Prajna Shetty, Ryan Rivas and Vagelis Hristidis "Correlating Health Insurance Plans' Ratings to Their Providers' Attributes" Journal of Medical Internet Research , 2016
Shiwen Cheng, Arash Termehchy, Vagelis Hristidis "Efficient Prediction of Difficult Keyword Queries over Databases" IEEE Transactions on Knowledge and Data Engineering (TKDE) , 2014 1041-4347
Shiwen Cheng, James Fang, Vagelis Hristidis, Harsha V. Madhyastha, Niluthpol Chowdhury Mithun, Dorian Perkins, Amit K. Roy-Chowdhury, Moloud Shahbazi, and Vassilis J. Tsotras "OSNI: Searching for Needles in a Haystack of Social Network Data" Demo at International Conference on Extending Database Technology (EDBT) , v.Demo , 2016
(Showing: 1 - 10 of 15)

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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