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.
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.
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.
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.
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.
Density-Based Clustering over an Evolving Data Stream with Noise.
Feng Cao;Martin Ester;Weining Qian;Aoying Zhou.
siam international conference on data mining (2006)
Security and Privacy in Cloud Computing: A Survey
Minqi Zhou;Rong Zhang;Wei Xie;Weining Qian.
semantics, knowledge and grid (2010)
PeerDB: a P2P-based system for distributed data sharing
W.S. Ng;B.C. Ooi;K.-L. Tan;Aoying Zhou.
international conference on data engineering (2003)
False positive or false negative: mining frequent itemsets from high speed transactional data streams
Jeffery Xu Yu;Zhihong Chong;Hongjun Lu;Aoying Zhou.
very large data bases (2004)
Dynamically maintaining frequent items over a data stream
Cheqing Jin;Weining Qian;Chaofeng Sha;Jeffrey X. Yu.
conference on information and knowledge management (2003)
VBI-Tree: A Peer-to-Peer Framework for Supporting Multi-Dimensional Indexing Schemes
H.V. Jagadish;Beng Chin Ooi;Quang Hieu Vu;Rong Zhang.
international conference on data engineering (2006)
Tracking clusters in evolving data streams over sliding windows
Aoying Zhou;Feng Cao;Weining Qian;Cheqing Jin.
Knowledge and Information Systems (2008)
Supporting multi-dimensional range queries in peer-to-peer systems
Yanfeng Shu;Beng Chin Ooi;Kian-Lee Tan;Aoying Zhou.
international conference on peer-to-peer computing (2005)
BiNE: Bipartite Network Embedding
Ming Gao;Leihui Chen;Xiangnan He;Aoying Zhou.
international acm sigir conference on research and development in information retrieval (2018)
Query processing of massive trajectory data based on mapreduce
Qiang Ma;Bin Yang;Weining Qian;Aoying Zhou.
cloud data management (2009)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
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