G
Gao Cong
Researcher at Nanyang Technological University
Publications - 237
Citations - 14241
Gao Cong is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 57, co-authored 218 publications receiving 11650 citations. Previous affiliations of Gao Cong include Microsoft & Aalborg University.
Papers
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Proceedings ArticleDOI
Diversity-Aware Top-k Publish/Subscribe for Text Stream
Lisi Chen,Gao Cong +1 more
TL;DR: This work proposes a novel solution to efficiently processing a large number of DAS queries over a stream of documents and demonstrates the efficiency of the approach on real-world dataset and experimental results show that the solution is able to achieve a reduction of the processing time by 60--75% compared with two baselines.
Proceedings ArticleDOI
Learning Travel Time Distributions with Deep Generative Model
TL;DR: A deep generative model to learn the travel time distribution for any route by conditioning on the real-time traffic, which produces substantially better results than state-of-the-art alternatives in two tasks: travel time estimation and route recovery from sparse trajectory data.
Journal ArticleDOI
Approaches to Exploring Category Information for Question Retrieval in Community Question-Answer Archives
TL;DR: This article presents several new approaches to exploiting the category information of questions for improving the performance of question retrieval, and it applies these approaches to existing question retrieval models, including a state-of-the-art question retrieval model.
Proceedings ArticleDOI
Annotation propagation revisited for key preserving views
TL;DR: This paper revisits the analysis of annotation propagation from source databases to views defined in terms of conjunctive (SPJ) queries and proposes a key preserving condition on SPJ views, which requires that the projection fields of an SPJ view Q retain a key of each base relation involved in Q.
Journal ArticleDOI
Retrieving regions of interest for user exploration
TL;DR: The length-constrained maximum-sum region (LCMSR) query is proposed that returns a spatial-network region that is located within a general region of interest, that does not exceed a given size constraint, and that best matches query keywords.