What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Database
- Programming language
His primary areas of study are Information retrieval, Data mining, Relational database, Database and Artificial intelligence.
Min Wang has researched Information retrieval in several fields, including Entity linking and Knowledge base.
In his work, Machine learning is strongly intertwined with Statistics, which is a subfield of Data mining.
His studies in Relational database integrate themes in fields like Computer security, Web server, Row and Sargable.
His study on Query language and Query optimization is often connected to Electronic business as part of broader study in Database.
The concepts of his Artificial intelligence study are interwoven with issues in Operator and Pattern recognition.
His most cited work include:
- Wavelet-based histograms for selectivity estimation (415 citations)
- Approximate computation of multidimensional aggregates of sparse data using wavelets (357 citations)
- Data cube approximation and histograms via wavelets (227 citations)
What are the main themes of his work throughout his whole career to date?
Min Wang mainly focuses on Information retrieval, Data mining, Query optimization, Database and Artificial intelligence.
In general Information retrieval, his work in Web query classification, Ontology and Web search query is often linked to Ontology linking many areas of study.
His Relational database study, which is part of a larger body of work in Data mining, is frequently linked to Provenance, bridging the gap between disciplines.
The Query optimization study combines topics in areas such as Query language, Query expansion, Online aggregation, Sargable and View.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Natural language processing, Machine learning and Pattern recognition.
His Set study integrates concerns from other disciplines, such as Real-time computing and Theoretical computer science.
He most often published in these fields:
- Information retrieval (40.71%)
- Data mining (37.14%)
- Query optimization (20.00%)
What were the highlights of his more recent work (between 2011-2018)?
- Information retrieval (40.71%)
- Data mining (37.14%)
- Artificial intelligence (14.29%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Information retrieval, Data mining, Artificial intelligence, Machine learning and Database.
His Information retrieval study combines topics from a wide range of disciplines, such as Entity linking and Knowledge base.
His Data mining research incorporates elements of Window and Information extraction.
His studies deal with areas such as Knowledge management and Natural language processing as well as Artificial intelligence.
His Database research is multidisciplinary, relying on both Cloud storage and SPARQL, RDF.
His Query optimization study which covers Query language that intersects with Concept search.
Between 2011 and 2018, his most popular works were:
- LINDEN: linking named entities with knowledge base via semantic knowledge (152 citations)
- Linking named entities in Tweets with knowledge base via user interest modeling (116 citations)
- Efficient multi-way theta-join processing using MapReduce (82 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Database
- Programming language
The scientist’s investigation covers issues in Information retrieval, Artificial intelligence, Knowledge base, Entity linking and Machine learning.
His work on Conjunctive query, Sargable and Web query classification as part of his general Information retrieval study is frequently connected to Context, thereby bridging the divide between different branches of science.
His work carried out in the field of Artificial intelligence brings together such families of science as Text mining and Natural language processing.
His Knowledge base study combines topics in areas such as Self-organizing list, Association list, List update problem, Social Semantic Web and Data Web.
His Entity linking research includes themes of Ontology, Ontology-based data integration, Upper ontology and Semantic Web.
His study in Machine learning is interdisciplinary in nature, drawing from both CRFS, Conditional random field, Rank and Inference.
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