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Wang-Chien Lee
Researcher at Pennsylvania State University
Publications - 367
Citations - 15328
Wang-Chien Lee is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Wireless sensor network & Mobile computing. The author has an hindex of 60, co-authored 366 publications receiving 14123 citations. Previous affiliations of Wang-Chien Lee include Ohio State University & Verizon Communications.
Papers
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Proceedings ArticleDOI
Scheduling web requests in broadcast environments
TL;DR: A novel scheduling algorithm, called Slack Inverse Number of requests (SIN), is proposed that takes into account the urgency and productivity of serving pending requests and significantly out performs existing algorithms over a wide range of workloads.
Proceedings ArticleDOI
Routing and Scheduling of Social Influence Diffusion in Online Social Networks
TL;DR: The needs of timely routing social influence are motivated to formulate a new optimization problem, namely, Routing and Scheduling of Target-Oriented Social Influence Diffusion (RAS-TOSID), and the Efficient Routing And Scheduling with Social and Temporal decOmposition (ERASSTO) algorithm is proposed.
Proceedings ArticleDOI
Data Access Techniques for Location-Based Services
TL;DR: This seminar will provide an overview of research issues arising from accessing of location-based services in a mobile computing environment and discuss the state-of-theart solutions.
Book ChapterDOI
Staffing Open Collaborative Projects Based on the Degree of Acquaintance
TL;DR: The problem defined is NP-hard and three algorithms are presented, namely, PSTA, STA and NFA, to solve the problem, and the results show the effectiveness of the proposed algorithms to find a well acquainted teams satisfying a given query.
Proceedings ArticleDOI
Location relevance classification for travelogue digests
TL;DR: A travelogue service to discover and convey various travelogue digests, in form of theme locations and geographical scope to their readers, and explores the textual and geographical features of locations to perform location relevance classification for theme location discovery.