R
Reynold Cheng
Researcher at University of Hong Kong
Publications - 192
Citations - 8947
Reynold Cheng is an academic researcher from University of Hong Kong. The author has contributed to research in topics: Uncertain data & Probabilistic logic. The author has an hindex of 44, co-authored 188 publications receiving 7717 citations. Previous affiliations of Reynold Cheng include University of New South Wales & Hong Kong Polytechnic University.
Papers
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Proceedings Article
Scalable Evaluation of k-NN Queries on Large Uncertain Graphs.
TL;DR: The U-tree is developed, a tree-based structure that produces a compact representation of G, based on which the k-NN query of q, which looks for k nodes in G whose distances from q are the shortest, can be executed efficiently.
Book ChapterDOI
Evaluating continuous probabilistic queries over imprecise sensor data
TL;DR: The probabilistic filter protocol is proposed, which governs remote sensor devices to decide upon whether values collected should be reported to the query server, which effectively reduces the communication and energy costs of sensor devices.
Journal ArticleDOI
A Crowdsourcing Framework for Collecting Tabular Data
TL;DR: T-Crowd is presented, which is a crowdsourcing system that considers attribute relationships and integrates each worker's answers on different attributes to effectively learn his/her trustworthiness and the true data values.
Proceedings ArticleDOI
Achieving low tail-latency and high scalability for serializable transactions in edge computing
Xusheng Chen,Haoze Song,Jianyu Jiang,Chaoyi Ruan,Cheng Li,Sen Wang,Gong Zhang,Reynold Cheng,Heming Cui +8 more
TL;DR: In this article, the authors present Dast (Decentralized Anticipate and Stretch), the first edge database that can meet the stringent performance requirements with serializability.
Proceedings ArticleDOI
A Convex-Programming Approach for Efficient Directed Densest Subgraph Discovery
TL;DR: This paper develops a convex-programming-based solution to the directed densest subgraph (DDS) problem by transforming the DDS problem into a set of linear programs based on the duality oflinear programs.