What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Database
His main research concerns Data mining, Cluster analysis, Data stream mining, Data stream clustering and Data stream.
His study in Data mining is interdisciplinary in nature, drawing from both Categorical variable, Bounded function and Outlier.
Cluster analysis is a primary field of his research addressed under Artificial intelligence.
The Data stream mining study which covers Transaction data that intersects with Reliability.
His Data stream clustering research integrates issues from Speedup, Pentium, Graphics hardware and General-purpose computing on graphics processing units.
The Pattern recognition study combines topics in areas such as Canopy clustering algorithm and Database index.
His most cited work include:
- Density-Based Clustering over an Evolving Data Stream with Noise. (638 citations)
- Security and Privacy in Cloud Computing: A Survey (362 citations)
- PeerDB: a P2P-based system for distributed data sharing (303 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Data mining, Information retrieval, Distributed computing, Database and Artificial intelligence.
Particularly relevant to Data stream mining is his body of work in Data mining.
His Information retrieval research is multidisciplinary, relying on both XML validation, XML, Efficient XML Interchange and XML database.
His research in Distributed computing intersects with topics in Workload, Scalability and Database transaction.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Natural language processing, Machine learning and Pattern recognition.
His research in Cluster analysis is mostly focused on Data stream clustering.
He most often published in these fields:
- Data mining (26.70%)
- Information retrieval (19.44%)
- Distributed computing (14.52%)
What were the highlights of his more recent work (between 2018-2021)?
- Distributed computing (14.52%)
- Artificial intelligence (12.41%)
- Natural language processing (3.75%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Distributed computing, Artificial intelligence, Natural language processing, Scalability and Database transaction.
His Distributed computing study integrates concerns from other disciplines, such as Workload, Data access, Schedule, Materialized view and Concurrency control.
His Artificial intelligence study frequently intersects with other fields, such as The Internet.
His research investigates the link between Scalability and topics such as Blockchain that cross with problems in Byzantine fault tolerance, Interface and Block.
His research investigates the connection with Key and areas like Neighbor algorithm which intersect with concerns in Data mining.
In general Data mining study, his work on Anomaly detection often relates to the realm of Focus, thereby connecting several areas of interest.
Between 2018 and 2021, his most popular works were:
- SEBDB: Semantics Empowered BlockChain DataBase (15 citations)
- A Family of Fuzzy Orthogonal Projection Models for Monolingual and Cross-lingual Hypernymy Prediction (8 citations)
- Predicting hypernym–hyponym relations for Chinese taxonomy learning (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Database
Aoying Zhou spends much of his time researching Artificial intelligence, Natural language processing, Relationship extraction, Knowledge graph and Graph.
His study in Data model extends to Artificial intelligence with its themes.
His Natural language processing research incorporates elements of Embedding, Word embedding, Relation and Taxonomy.
His Word embedding research incorporates elements of Knowledge representation and reasoning, Information retrieval, Automatic summarization and Semantic network.
His biological study spans a wide range of topics, including Text corpus, Deep learning, The Internet and Data science.
His research in Graph intersects with topics in Operations research, Interval and Concatenation.
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