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 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.
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.
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.
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.
The priority R-tree: A practically efficient and worst-case optimal R-tree
Lars Arge;Mark De Berg;Herman Haverkort;Ke Yi.
ACM Transactions on Algorithms (2008)
Finding frequent items in probabilistic data
Qin Zhang;Feifei Li;Ke Yi.
international conference on management of data (2008)
An Information-Theoretic Approach to Detecting Changes in Multi-Dimensional Data Streams
Tamraparni Dasu;Shankar Krishnan;Suresh Venkatasubramanian;Ke Yi.
Proc. Symposium on the Interface of Statistics, Computing Science, and Applications (Interface) (2006)
Quality and efficiency in high dimensional nearest neighbor search
Yufei Tao;Ke Yi;Cheng Sheng;Panos Kalnis.
international conference on management of data (2009)
Semantics of Ranking Queries for Probabilistic Data and Expected Ranks
Graham Cormode;Feifei Li;Ke Yi.
international conference on data engineering (2009)
Tree indexing on solid state drives
Yinan Li;Bingsheng He;Robin Jun Yang;Qiong Luo.
very large data bases (2010)
Algorithms for distributed functional monitoring
Graham Cormode;S. Muthukrishnan;Ke Yi.
symposium on discrete algorithms (2008)
Sliding-window top-k queries on uncertain streams
Cheqing Jin;Ke Yi;Lei Chen;Jeffrey Xu Yu.
very large data bases (2008)
Pankaj K. Agarwal;Graham Cormode;Zengfeng Huang;Jeff M. Phillips.
symposium on principles of database systems (2012)
Efficient maintenance of materialized top-k views
Ke Yi;Hai Yu;Jun Yang;Gangqiang Xia.
international conference on data engineering (2003)
Profile was last updated on December 6th, 2021.
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