Research

Yongping Du

Du Yongping is associate professor and supervisor for master candidates at Beijing University of Technology. Dr. Du received her Ph.D degree from Fudan University in 2005. She has published more than 30 academic papers in premier journals and international conference.
Her research interests include Information Retrieval, Data Mining, Social Network, and Natural Language Processing.
 

Weimao Ke's paper receives best paper nomination at JCDL

One research paper authored by Weimao Ke has been nominated for the Vannevar Bush Best Paper Award for the ACM/IEEE Joint Conference on Digital Libraries (JCDL'13).
Ke, Weimao. "Information-theoretic Term Weighting Schemes for Document Clustering." In ACM/IEEE Joint Conference on Digital Libraries, 1-10., 2013.

Hadoop Data Explorer

 

The system prototype interface is available at: 

http://lincs.ischool.drexel.edu/sgbrowser/hdex/

 

Large-scale Data Exploration

Hadoop is a powerful framework for processing large-scale data but works primarily in the batch mode without user interaction. There are many scenarios in which users such as business analysts and data scientists need to:

Papers of LiNCS researchers accepted to ACM/IEEE JCDL

HomeOne research paper authored by Weimao Ke and the other first-authored by Xuemei Gong have been accepted to the ACM/IEEE Joint Conference on Digital Libraries (JCDL'13).

Paper of LiNCS researchers accepted to JASIST

Web search engines are the gateway for users to access health information. This study explored whether a search interface based on the Bing API and enabled by Scatter/Gather, a well-known document clustering technique, can improve health information search. Forty participants without medical background were randomly assigned to two interfaces, a baseline interface that resembles typical Web search engines and a Scatter/Gather interface. Both groups performed two lookup and two exploratory health-related tasks.

Weimao Ke and Javed Mostafa publish in ACM TOIS

With the ubiquitous production, distribution and consumption of information, today’s digital envi- ronments such as the Web are becoming increasingly large and decentralized. It is hardly possible to obtain central control over information collections and systems in these environments. Search- ing for information in these information spaces has brought about problems beyond traditional boundaries of information retrieval (IR) research.

Weimao Ke publishes in the Scientometrics journal

We propose a model to analyze citation growth and influences of fitness (competitiveness) factors in an evolving citation network. Applying the proposed method to modeling citations to papers and scholars in the InfoVis 2004 data, a benchmark collection about a 31-year history of information visualization, leads to findings consistent with citation distributions in general and observations of the domain in particular. Fitness variables based on prior impacts and the time factor have significant influences on citation outcomes.

Scatter/Gather Searching and Browsing with Bing API

Scatter/Gather Browser on TREC HARD track (news)

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