What is he best known for?
The fields of study he is best known for:
- Algorithm
- Artificial intelligence
- Database
Theoretical computer science, Data mining, Tuple, Algorithm and Tree are his primary areas of study.
His Data mining research includes themes of STREAMS and Artificial intelligence.
His Algorithm research is multidisciplinary, incorporating elements of Multiset and Nearest neighbor search.
The various areas that Ke Yi examines in his Nearest neighbor search study include Locality-sensitive hashing and k-nearest neighbors algorithm.
Ke Yi has researched Tree in several fields, including Asymptotically optimal algorithm and Combinatorics.
The study incorporates disciplines such as Ranking, Probabilistic logic and Range query in addition to Uncertain data.
His most cited work include:
- Quality and efficiency in high dimensional nearest neighbor search (181 citations)
- Semantics of Ranking Queries for Probabilistic Data and Expected Ranks (181 citations)
- Finding frequent items in probabilistic data (169 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Theoretical computer science, Algorithm, Combinatorics, Data mining and Discrete mathematics.
His studies deal with areas such as Uncertain data, Computation and Approximation algorithm as well as Theoretical computer science.
His Algorithm research is multidisciplinary, incorporating perspectives in Hash function and Data set.
His research investigates the connection between Combinatorics and topics such as Asymptotically optimal algorithm that intersect with problems in R+ tree.
His Data mining study integrates concerns from other disciplines, such as Ranking and Scalability.
His work on Best bin first as part of his general Nearest neighbor search study is frequently connected to Fixed-radius near neighbors, thereby bridging the divide between different branches of science.
He most often published in these fields:
- Theoretical computer science (27.78%)
- Algorithm (23.61%)
- Combinatorics (20.14%)
What were the highlights of his more recent work (between 2016-2021)?
- Algorithm (23.61%)
- Theoretical computer science (27.78%)
- Massively parallel (2.78%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Algorithm, Theoretical computer science, Massively parallel, Parallel algorithm and Spark.
The Algorithm study combines topics in areas such as Bottleneck and Data set.
His Theoretical computer science research incorporates themes from Acknowledgement and Simple random sample.
As part of one scientific family, Ke Yi deals mainly with the area of Massively parallel, narrowing it down to issues related to the Server, and often Massively parallel computation and Combinatorics.
His biological study spans a wide range of topics, including Hypergraph, Tree, Semiring and Aggregate.
Ke Yi undertakes interdisciplinary study in the fields of Sequence and Tuple through his works.
Between 2016 and 2021, his most popular works were:
- Two-Level Sampling for Join Size Estimation (37 citations)
- Random Sampling over Joins Revisited (31 citations)
- On Improving Wireless Channel Utilization: A Collision Tolerance-Based Approach (22 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Artificial intelligence
- Computer network
His main research concerns Theoretical computer science, Algorithm, Spark, Cartesian product and Database theory.
He has included themes like Simple random sample, Graph and SQL in his Theoretical computer science study.
His Parallel algorithm and Analysis of parallel algorithms study, which is part of a larger body of work in Algorithm, is frequently linked to Similarity and Quadratic equation, bridging the gap between disciplines.
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