What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- Machine learning
Wee Keong Ng mainly focuses on Data mining, Computer security, Data stream mining, Information retrieval and World Wide Web.
Wee Keong Ng is involved in the study of Data mining that focuses on Association rule learning in particular.
The Data stream mining study combines topics in areas such as Data stream clustering, Concept mining, Feature selection and Cluster analysis.
Wee Keong Ng combines subjects such as cXML, XML validation, Web page, Efficient XML Interchange and Simple API for XML with his study of Information retrieval.
His study in World Wide Web focuses on Web navigation, Web standards, Web mapping, Web intelligence and Web 2.0.
His Data Web research is multidisciplinary, incorporating perspectives in Static web page and Web query classification.
His most cited work include:
- A survey of Web metrics (220 citations)
- Research Issues in Web Data Mining (202 citations)
- Web classification using support vector machine (183 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Data mining, World Wide Web, Web page, Database and Data Web.
His Data mining study integrates concerns from other disciplines, such as Scalability and Cluster analysis, Artificial intelligence.
His World Wide Web study incorporates themes from Data modeling and Information retrieval.
His research investigates the connection between Database and topics such as Encryption that intersect with problems in Server, Theoretical computer science and Protocol.
The study incorporates disciplines such as Static web page, Web development, Web navigation and Web modeling in addition to Data Web.
The Web development study which covers Web 2.0 that intersects with Web application security.
He most often published in these fields:
- Data mining (27.76%)
- World Wide Web (23.13%)
- Web page (17.08%)
What were the highlights of his more recent work (between 2012-2021)?
- Encryption (7.47%)
- Scalability (8.90%)
- Computer security (7.12%)
In recent papers he was focusing on the following fields of study:
Encryption, Scalability, Computer security, Database and Information privacy are his primary areas of study.
His Encryption research includes elements of Theoretical computer science, Cryptography, Outsourcing, Protocol and Server.
His studies deal with areas such as Data mining, Database transaction, Blockchain, Smart contract and Artificial intelligence as well as Scalability.
His Data mining study combines topics in areas such as Wireless sensor network, Decision boundary, Support vector machine, Hyperplane and Optimization problem.
In general Artificial intelligence, his work in Adaptive learning algorithm and Cluster analysis is often linked to Online learning linking many areas of study.
His research in the fields of Relational database, Distributed data store and Database server overlaps with other disciplines such as Computer data storage.
Between 2012 and 2021, his most popular works were:
- A survey on data stream clustering and classification (146 citations)
- Blockchain-Based System for Secure Data Storage with Private Keyword Search (52 citations)
- Encrypted Set Intersection Protocol for Outsourced Datasets (23 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Database
His scientific interests lie mostly in Encryption, Scalability, Computer security, Database and Data mining.
His Encryption research incorporates themes from Outsourcing and Information sensitivity.
His work carried out in the field of Scalability brings together such families of science as Distributed computing, Protocol, Information privacy, Server and Big data.
Wee Keong Ng has researched Server in several fields, including Association rule learning, Overhead and Public-key cryptography.
His study in the field of Distributed data store also crosses realms of Computer data storage.
His Data mining research is multidisciplinary, relying on both Wireless sensor network, Face, Cluster analysis, Data stream clustering and Focus.
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