What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Database
His primary areas of investigation include Data mining, Distributed computing, Scalability, Artificial intelligence and Query optimization.
In general Data mining, his work in Association rule learning is often linked to Space linking many areas of study.
His work deals with themes such as Structure, Server and Data management, which intersect with Distributed computing.
Kian-Lee Tan has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition.
As part of one scientific family, he deals mainly with the area of Pattern recognition, narrowing it down to issues related to the Partition, and often iDistance and Search engine indexing.
His Query optimization research focuses on subjects like Sargable, which are linked to View, Query by Example, Conjunctive query and Query language.
His most cited work include:
- Efficient Progressive Skyline Computation (680 citations)
- Private queries in location based services: anonymizers are not necessary (663 citations)
- iDistance: An adaptive B+-tree based indexing method for nearest neighbor search (500 citations)
What are the main themes of his work throughout his whole career to date?
Data mining, Distributed computing, Database, Information retrieval and Set are his primary areas of study.
Kian-Lee Tan works mostly in the field of Data mining, limiting it down to topics relating to Theoretical computer science and, in certain cases, Graph.
His Distributed computing research incorporates themes from Scalability and Computer network, Server, Cache.
His Scalability study frequently links to other fields, such as Peer-to-peer.
His study in Query optimization, Sargable and Query language is carried out as part of his studies in Information retrieval.
His work is dedicated to discovering how Query optimization, Web search query are connected with Query expansion and other disciplines.
He most often published in these fields:
- Data mining (28.72%)
- Distributed computing (18.80%)
- Database (13.64%)
What were the highlights of his more recent work (between 2015-2021)?
- Distributed computing (18.80%)
- Set (10.54%)
- Data mining (28.72%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Distributed computing, Set, Data mining, Graph and Information retrieval.
His research in Distributed computing is mostly focused on Distributed database.
His Set research integrates issues from Maximization, Database, Social network, Sliding window protocol and Ranking.
His work carried out in the field of Data mining brings together such families of science as Task analysis and Index.
His research in Graph intersects with topics in Theoretical computer science and Parallel computing.
He is studying Relational database, which is a component of Information retrieval.
Between 2015 and 2021, his most popular works were:
- BLOCKBENCH: A Framework for Analyzing Private Blockchains (388 citations)
- Influence Maximization on Social Graphs: A Survey (163 citations)
- Database Meets Deep Learning: Challenges and Opportunities (67 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Database
His primary areas of study are Data mining, Distributed computing, Social network, Parallel computing and Scalability.
His Data mining research incorporates elements of Field and Task.
As a part of the same scientific family, Kian-Lee Tan mostly works in the field of Distributed computing, focusing on Source code and, on occasion, Distributed data store, Component, Cloud computing, Query optimization and Asynchronous communication.
His Social network research includes themes of Set, Maximization and Data science.
His research integrates issues of Theoretical computer science, Graph analytics, Graph algorithms, Graph and Bottleneck in his study of Parallel computing.
His research investigates the connection with Usability and areas like Artificial intelligence which intersect with concerns in Database.
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