What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Database
- Statistics
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.
His most cited work include:
- Evaluating probabilistic queries over imprecise data (565 citations)
- Querying imprecise data in moving object environments (401 citations)
- Preserving user location privacy in mobile data management infrastructures (279 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Data mining (36.84%)
- Theoretical computer science (26.32%)
- Probabilistic logic (23.16%)
What were the highlights of his more recent work (between 2018-2021)?
- Graph (17.89%)
- Theoretical computer science (26.32%)
- Vertex (6.84%)
In recent papers he was focusing on the following fields of study:
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.
Between 2018 and 2021, his most popular works were:
- A survey of community search over big graphs (48 citations)
- On Spatial-Aware Community Search (26 citations)
- Efficient algorithms for densest subgraph discovery (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Database
- Statistics
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.
