J
Jun Liu
Researcher at Beijing University of Posts and Telecommunications
Publications - 71
Citations - 720
Jun Liu is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Cellular network & Mobile computing. The author has an hindex of 10, co-authored 68 publications receiving 494 citations.
Papers
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Journal ArticleDOI
Monitoring and analyzing big traffic data of a large-scale cellular network with Hadoop
Jun Liu,Feng Liu,Nirwan Ansari +2 more
TL;DR: A traffic monitoring and analysis system for large-scale networks based on Hadoop, an open-source distributed computing platform for big data processing on commodity hardware, has been deployed in the core network of a large cellular network and extensively evaluated.
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Big Data Driven Hidden Markov Model Based Individual Mobility Prediction at Points of Interest
TL;DR: This paper investigates the effect of living habits on the models of spatio-temporal prediction and next-place prediction, and selects one from these two models for an individual to achieve effective mobility prediction at users’ points of interest.
Journal ArticleDOI
Characterizing and Predicting the Popularity of Online Videos
Chenyu Li,Jun Liu,Shuxin Ouyang +2 more
TL;DR: This paper addresses the challenge of future popularity prediction, by proposing a model that can capture the popularity dynamics based on early popularity evolution pattern and future popularity burst prediction and achieves significant decreases in relative prediction errors.
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Spark-Based Large-Scale Matrix Inversion for Big Data Processing
Jun Liu,Yang Liang,Nirwan Ansari +2 more
TL;DR: A LU decomposition based block-recursive algorithm for large-scale matrix inversion and its well-designed implementation with optimized data structure, reduction of space complexity and effective matrix multiplication on the Spark parallel computing platform are presented.
Journal ArticleDOI
Privacy preserving distributed data mining based on secure multi-party computation
TL;DR: This paper designs algorithms based on optimized matrix computation with one-hot encoding and LU decomposition to support these requirements in the MPC context and implements them based on a SPDZ protocol, a computation framework of MPC.