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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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Journal ArticleDOI
Activity Organization for Friend-Making Optimization in Online Social Networks
TL;DR: It is proved that HMGF is NP-Hard, and there exists no approximation algorithm for it unless P=NP, and an error-bounded approximation algorithm is proposed, MaxGF
Proceedings ArticleDOI
SearchGen: a synthetic workload generator for scientific literature digital libraries and search engines
TL;DR: Analysis of usage logs of CiteSeer, a scientific literature digital library and search engine, finds the access intervals show high variance, and thus cannot be predicted well with time-series models, and a comparison between synthetic workloads and actual logged traces suggests that the synthetic workload fits well.
Journal ArticleDOI
Range-Based Nearest Neighbor Queries with Complex-Shaped Obstacles
TL;DR: An efficient algorithm is proposed, which exploits the OB-tree and a binary traversal order of data objects to accelerate query processing of RONN, and the experimental result shows that the RRONN-OBA algorithm outperforms the two R-tree based algorithms and RONn-OA significantly.
Book ChapterDOI
Patent Evaluation Based on Technological Trajectory Revealed in Relevant Prior Patents
TL;DR: Experimental results show that the models created based on the proposed approach significantly enhance those using the baseline features or patent backward citations, and also exploit trends in temporal patterns of relevant prior patents, which are highly related to patent values.
Proceedings ArticleDOI
Social-Aware VR Configuration Recommendation via Multi-Feedback Coupled Tensor Factorization
TL;DR: This paper designs the Multiview-Enabled Configuration Ranking System (MEIRS) that first extracts discriminative features based on Marketing theories and then introduces a new coupled tensor factorization model to learn the representation of users, Multi-View Display (MVD) configurations, and multiple feedback with content features.