What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Database
- Operating system
His scientific interests lie mostly in Data mining, Uncertain data, Database, Artificial intelligence and Association rule learning.
A large part of his Data mining studies is devoted to Online analytical processing.
His Uncertain data study combines topics from a wide range of disciplines, such as Cluster analysis and Pruning.
In Database, Ben Kao works on issues like Encryption, which are connected to Secure two-party computation and Private information retrieval.
His work in Artificial intelligence addresses subjects such as Machine learning, which are connected to disciplines such as Software.
His Association rule learning research includes elements of Information retrieval and Data science.
His most cited work include:
- Secure kNN computation on encrypted databases (516 citations)
- A General Incremental Technique for Maintaining Discovered Association Rules (351 citations)
- Mining frequent itemsets from uncertain data (294 citations)
What are the main themes of his work throughout his whole career to date?
Ben Kao focuses on Data mining, Artificial intelligence, Database, Algorithm and Theoretical computer science.
The concepts of his Data mining study are interwoven with issues in Set and Cluster analysis.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning, Pattern recognition and Natural language processing.
His work on Concurrency control, Database transaction, Optimistic concurrency control and Database testing as part of his general Database study is frequently connected to Service provider, thereby bridging the divide between different branches of science.
His work in Theoretical computer science tackles topics such as Graph which are related to areas like Graph.
In his study, Efficient algorithm is strongly linked to Pruning, which falls under the umbrella field of Uncertain data.
He most often published in these fields:
- Data mining (33.11%)
- Artificial intelligence (17.22%)
- Database (17.22%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (17.22%)
- Data mining (33.11%)
- Graph (5.30%)
In recent papers he was focusing on the following fields of study:
Ben Kao spends much of his time researching Artificial intelligence, Data mining, Graph, Theoretical computer science and Cluster analysis.
Ben Kao interconnects Machine learning and Natural language processing in the investigation of issues within Artificial intelligence.
Ben Kao studies Machine learning, namely Uncertain data.
His work on Uncertain data mining and Measure is typically connected to Throughput, Focus and Heterogeneous information as part of general Data mining study, connecting several disciplines of science.
His Theoretical computer science research incorporates themes from Canonicalization and Relation.
His biological study deals with issues like Pattern recognition, which deal with fields such as Robustness and Lasso.
Between 2017 and 2021, his most popular works were:
- Towards Practical Open Knowledge Base Canonicalization (15 citations)
- Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT (14 citations)
- Spectral Clustering in Heterogeneous Information Networks (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Database
- Operating system
His primary areas of study are Artificial intelligence, Natural language processing, Spectral clustering, Cluster analysis and Data mining.
The study of Artificial intelligence is intertwined with the study of Masking in a number of ways.
His work deals with themes such as Knowledge base and PERQ, which intersect with Natural language processing.
His biological study spans a wide range of topics, including Reachability, Range, Optimization problem, Robustness and Pattern recognition.
His Cluster analysis study frequently links to adjacent areas such as Construct.
His Data mining research is multidisciplinary, incorporating perspectives in Query throughput, Index and Synthetic data.
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