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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.

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Improving Data Analytics with Fast and Adaptive Regularization

TL;DR: This paper proposes a general adaptive regularization method based on Gaussian Mixture to learn the best regularization function according to the observed parameters, and develops an effective update algorithm which integrates Expectation Maximization with Stochastic Gradient Descent.
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Signature path dictionary for nested object query processing

TL;DR: A new method is introduced, the signature path dictionary, which combines signature techniques with the path dictionary organization designed for fast object traversals and derives cost formulae for its storage overhead as well as the retrieval and update costs.
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On Extracting Socially Tenuous Groups for Online Social Networks with k-Triangles

TL;DR: In this paper, the authors introduced the notion of k-triangles to measure the tenuity of a group and formulated a new research problem, Minimum k-Triangle Disconnected Group with No-Pair Constraint, to find a socially tenuous group from the online social network.
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Workload analysis for scientific literature digital libraries

TL;DR: This study presents an extensive analysis into the workload of scientific literature digital libraries, unveiling their temporal and user interest patterns and investigates how to utilize the findings to improve system performance.
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Distributed in-memory processing of All K Nearest Neighbor queries

TL;DR: This paper evaluates, both analytically and experimentally, how the pruning efficiency of the Spitfire algorithm plays a pivotal role in reducing communication and response time up to an order of magnitude, compared to three other state-of-the-art distributed AkNN algorithms executed in distributed main-memory.