Reynold Cheng mainly focuses on Data mining, Probabilistic logic, Uncertain data, Theoretical computer science and Probabilistic database. His Association rule learning study, which is part of a larger body of work in Data mining, is frequently linked to Biological database, bridging the gap between disciplines. His Probabilistic logic research includes themes of Location-based service, Object, Information retrieval, Query optimization and Mobile computing.
His studies in Uncertain data integrate themes in fields like Data modeling, Probability density function, Overhead, Pruning and Cluster analysis. His Theoretical computer science study combines topics in areas such as Search engine indexing and Graph. In his study, Database schema, Query language, Web search query and Online aggregation is strongly linked to View, which falls under the umbrella field of Probabilistic database.
Reynold Cheng spends much of his time researching Data mining, Theoretical computer science, Probabilistic logic, Uncertain data and Graph. His research investigates the connection with Data mining and areas like Overhead which intersect with concerns in Pruning. His Theoretical computer science research incorporates themes from Scalability and k-nearest neighbors algorithm.
His Probabilistic logic study combines topics from a wide range of disciplines, such as Probabilistic database and Query optimization, Information retrieval, Database. His research in Uncertain data intersects with topics in Algorithm, Probability density function and Cluster analysis. Reynold Cheng combines subjects such as Graph and Biological network with his study of Graph.
Graph, Theoretical computer science, Vertex, Graph and Biological network are his primary areas of study. His Graph research integrates issues from Approximation algorithm, Directed graph and Big data. His study looks at the relationship between Approximation algorithm and fields such as Cluster analysis, as well as how they intersect with chemical problems.
The Theoretical computer science study combines topics in areas such as Correctness, Search engine indexing and k-nearest neighbors algorithm. His Graph research incorporates elements of Matching and Euclidean distance. He merges Throughput with Data mining in his research.
His main research concerns Graph, Theoretical computer science, Community search, Vertex and Biological network. His Graph research is multidisciplinary, incorporating perspectives in Point, Thesaurus, Data science and Big data. The study incorporates disciplines such as Matching, Common spatial pattern and Graph in addition to Theoretical computer science.
His Community search research is multidisciplinary, relying on both Approximation algorithm and Information retrieval. His work carried out in the field of Approximation algorithm brings together such families of science as Analytics and Cluster analysis. He studies Vertex, namely Vertex connectivity.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Evaluating probabilistic queries over imprecise data
Reynold Cheng;Dmitri V. Kalashnikov;Sunil Prabhakar.
international conference on management of data (2003)
Querying imprecise data in moving object environments
R. Cheng;D.V. Kalashnikov;S. Prabhakar.
international conference on data engineering (2003)
Preserving user location privacy in mobile data management infrastructures
Reynold Cheng;Yu Zhang;Elisa Bertino;Sunil Prabhakar.
privacy enhancing technologies (2006)
Indexing multi-dimensional uncertain data with arbitrary probability density functions
Yufei Tao;Reynold Cheng;Xiaokui Xiao;Wang Kay Ngai.
very large data bases (2005)
Efficient indexing methods for probabilistic threshold queries over uncertain data
Reynold Cheng;Yuni Xia;Sunil Prabhakar;Rahul Shah.
very large data bases (2004)
Efficient Clustering of Uncertain Data
Wang Ngai;Ben Kao;Chun Chui;Reynold Cheng.
international conference on data mining (2006)
Uncertain data mining: an example in clustering location data
Michael Chau;Reynold Cheng;Ben Kao;Jackey Ng.
knowledge discovery and data mining (2006)
Truth inference in crowdsourcing: is the problem solved?
Yudian Zheng;Guoliang Li;Yuanbing Li;Caihua Shan.
very large data bases (2017)
Naive Bayes Classification of Uncertain Data
Jiangtao Ren;Sau Dan Lee;Xianlu Chen;Ben Kao.
international conference on data mining (2009)
Probabilistic Verifiers: Evaluating Constrained Nearest-Neighbor Queries over Uncertain Data
R. Cheng;Jinchuan Chen;M. Mokbel;Chi-Yin Chow.
international conference on data engineering (2008)
Profile was last updated on December 6th, 2021.
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