24 Papers
80 Citations
Wuman Luo is an academic researcher from Hong Kong University of Science and Technology. The author has contributed to research in topics: Computer science & Scalability. The author has an hindex of 8, co-authored 14 publications. Previous affiliations of Wuman Luo include University of Macau.
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Papers
MR-DBSCAN: An Efficient Parallel Density-Based Clustering Algorithm Using MapReduce
Yaobin He,Haoyu Tan,Wuman Luo,Huajian Mao,Di Ma,Shengzhong Feng,Jianping Fan +6 more
- 07 Dec 2011
TL;DR: This paper proposes an efficient parallel density-based clustering algorithm and implements it by a 4-stages MapReduce paradigm and adopts a quick partitioning strategy for large scale non-indexed data.
Finding time period-based most frequent path in big trajectory data
Wuman Luo,Haoyu Tan,Lei Chen,Lionel M. Ni +3 more
- 22 Jun 2013
TL;DR: A new path finding query which finds the most frequent path (MFP) during user-specified time periods in large-scale historical trajectory data and proposes efficient search algorithms together with novel indexes to speed up the processing of TPMFP.
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Machine learning-driven credit risk: a systemic review
TL;DR: In this paper , a series of major research contributions (76 papers) over the past eight years using statistical, machine learning and deep learning techniques to address the problems of credit risk are systematically reviewed.
Efficient Similarity Joins on Massive High-Dimensional Datasets Using MapReduce
Wuman Luo,Haoyu Tan,Huajian Mao,Lionel M. Ni +3 more
- 23 Jul 2012
TL;DR: A cost model is proposed to demonstrate that it is important to take both communication and computation costs into account as dimensionality and data volume increases and DAA (Dimension Aggregation Approximation) is proposed, an efficient compression approach that can help significantly reduce both these costs when performing parallel HDSJs.
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Clockwise compression for trajectory data under road network constraints
Yudian Ji,Yuda Zang,Wuman Luo,Xibo Zhou,Ye Ding,Lionel M. Ni +5 more
- 01 Dec 2016
TL;DR: A novel compression framework called Clockwise Compression Framework (CCF) is proposed for big trajectory data compression under road network constraints and shows promising performances in various metrics and outperforms the state-of-the-art methods.
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