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Bin Yao
Researcher at Shanghai Jiao Tong University
Publications - 84
Citations - 2058
Bin Yao is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Spatial query & Tree (data structure). The author has an hindex of 20, co-authored 79 publications receiving 1540 citations. Previous affiliations of Bin Yao include Shenzhen University & Florida State University.
Papers
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Proceedings ArticleDOI
Simba: Efficient In-Memory Spatial Analytics
TL;DR: Simba is a scalable and efficient in-memory spatial query processing and analytics for big spatial data that extends the Spark SQL engine to support rich spatial queries and analytics through both SQL and the DataFrame API.
Proceedings ArticleDOI
Secure nearest neighbor revisited
Bin Yao,Feifei Li,Xiaokui Xiao +2 more
TL;DR: New SNN methods are designed, which provide customizable tradeoff between efficiency and communication cost, and are as secure as the encryption scheme E used to encrypt the query and the database, where E can be any well-established encryption schemes.
Proceedings ArticleDOI
Optimal location queries in road network databases
Xiaokui Xiao,Bin Yao,Feifei Li +2 more
TL;DR: A unified framework is proposed that addresses three variants of OL queries that find important applications in practice, and is instantiate the framework with several novel query processing algorithms.
Proceedings ArticleDOI
Trichromatic Online Matching in Real-Time Spatial Crowdsourcing
TL;DR: This paper formally defines a novel dynamic online task assignment problem, called the trichromatic online matching in real-time spatial crowdsourcing (TOM) problem, which is proven to be NP-hard and presents a threshold-based randomized algorithm that not only guarantees a tighter competitive ratio but also includes an adaptive optimization technique, which can quickly learn the optimal threshold for the randomized algorithm.
Proceedings ArticleDOI
K nearest neighbor queries and kNN-Joins in large relational databases (almost) for free
Bin Yao,Feifei Li,Piyush Kumar +2 more
TL;DR: This work designs algorithms that could be implemented by SQL operators without changes to the database engine, hence enabling the query optimizer to understand and generate the “best” query plan.