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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Book ChapterDOI
A Framework for Conditioning Uncertain Relational Data
TL;DR: A framework for representing conditioned probabilistic relational data, in which the existence of tuples in possible worlds is determined by Boolean expressions composed from elementary events, and a general algorithm for this computation is devised and presented.
Book ChapterDOI
Efficient Top-k Subscription Matching for Location-Aware Publish/Subscribe
TL;DR: The main idea is to develop a location-aware version of the Pub/Sub model, which was designed for message dissemination, that can effectively and efficiently return the top-k subscriptions with respect to an event.
Proceedings ArticleDOI
CubeLSI: An effective and efficient method for searching resources in social tagging systems
TL;DR: This work proposes CubeLSI — a technique that extends traditional LSI to include taggers as another dimension of feature space of resources, and compares it against a number of other tag-based retrieval models and shows that it significantly outperforms the other models in terms of retrieval accuracy.
Journal ArticleDOI
A statistics-based sensor selection scheme for continuous probabilistic queries in sensor networks
TL;DR: This paper proposes a statistical approach to decide which sensor nodes to be used to answer a query, and presents methods to select an appropriate set of sensors and provide reliable answers for several common aggregate queries.
Book ChapterDOI
Quality-Aware Probing of Uncertain Data with Resource Constraints
Jinchuan Chen,Reynold Cheng +1 more
TL;DR: An entropy-based metric is presented to quantify the degree of ambiguity of probabilistic query answers due to data uncertainty and a new method to improve the query answer quality is developed.