What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Database
Xuemin Lin mostly deals with Data mining, Theoretical computer science, Skyline, Graph and Uncertain data.
His Data mining research includes themes of Data modeling, Object, Information retrieval, Ranking and Web search query.
His Theoretical computer science research includes elements of Euclidean space, Modular decomposition, Similarity, Algorithm and Nearest neighbor search.
His study in Skyline is interdisciplinary in nature, drawing from both Distance based, Scalability, Database theory, Cube and Data stream.
His Graph study incorporates themes from Time complexity, Graph and Directed graph.
His work carried out in the field of Uncertain data brings together such families of science as Sliding window protocol, Wireless sensor network, STREAMS, Probabilistic logic and Probabilistic database.
His most cited work include:
- Efficient similarity joins for near duplicate detection (447 citations)
- Probabilistic skylines on uncertain data (440 citations)
- Selecting Stars: The k Most Representative Skyline Operator (342 citations)
What are the main themes of his work throughout his whole career to date?
Xuemin Lin mainly investigates Data mining, Theoretical computer science, Graph, Algorithm and Computation.
In his study, Index is strongly linked to Search engine indexing, which falls under the umbrella field of Data mining.
His study on Theoretical computer science also encompasses disciplines like
- Time complexity which connect with Mathematical optimization,
- Speedup which intersects with area such as Bipartite graph.
The study incorporates disciplines such as Graph theory and Graph in addition to Graph.
His work on Algorithm is being expanded to include thematically relevant topics such as k-nearest neighbors algorithm.
Uncertain data is frequently linked to Probabilistic database in his study.
He most often published in these fields:
- Data mining (34.93%)
- Theoretical computer science (31.22%)
- Graph (23.14%)
What were the highlights of his more recent work (between 2018-2021)?
- Graph (23.14%)
- Theoretical computer science (31.22%)
- Vertex (10.70%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Graph, Theoretical computer science, Vertex, Computation and Speedup.
He has researched Graph in several fields, including Algorithm, Graph and Search engine indexing.
His Search engine indexing research is multidisciplinary, incorporating elements of Data mining, Distance labeling, Bounded function, Epigraph and Scaling.
His studies deal with areas such as Group, Power graph analysis, Matching, Index and Knowledge graph as well as Theoretical computer science.
In his study, which falls under the umbrella issue of Vertex, Natural language, Ranking and Machine learning is strongly linked to Path.
The concepts of his Speedup study are interwoven with issues in Time complexity, Scalability, Bipartite graph and Pruning.
Between 2018 and 2021, his most popular works were:
- Approximate Nearest Neighbor Search on High Dimensional Data — Experiments, Analyses, and Improvement (63 citations)
- A survey of community search over big graphs (48 citations)
- Efficient algorithms for densest subgraph discovery (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Database
Graph, Theoretical computer science, Computation, Vertex and Speedup are his primary areas of study.
Xuemin Lin works mostly in the field of Graph, limiting it down to topics relating to Scalability and, in certain cases, Graph theory.
The Theoretical computer science study combines topics in areas such as Distributed algorithm, Degree, Knowledge graph, Efficient algorithm and Complex network.
His Computation study combines topics in areas such as Core, Approximation algorithm and Approximation theory.
Xuemin Lin studied Core and Decomposition that intersect with Algorithm.
His Speedup study also includes fields such as
- Search engine indexing which is related to area like Bounded function, Small-world network, Distance labeling and Scaling,
- Bipartite graph which connect with Time complexity.
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