D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 32 Citations 5,334 118 World Ranking 7323 National Ranking 3464

Research.com Recognitions

Awards & Achievements

2012 - Fellow of Biomaterials Science and Engineering

2012 - Fellow of the Indian National Academy of Engineering (INAE)

Overview

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.

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.

Best Publications

Wavelet-based histograms for selectivity estimation

Yossi Matias;Jeffrey Scott Vitter;Min Wang.
international conference on management of data (1998)

563 Citations

Approximate computation of multidimensional aggregates of sparse data using wavelets

Jeffrey Scott Vitter;Min Wang.
international conference on management of data (1999)

491 Citations

Data cube approximation and histograms via wavelets

Jeffrey Scott Vitter;Min Wang;Bala Iyer.
conference on information and knowledge management (1998)

326 Citations

LINDEN: linking named entities with knowledge base via semantic knowledge

Wei Shen;Jianyong Wang;Ping Luo;Min Wang.
the web conference (2012)

263 Citations

Dynamic Maintenance of Wavelet-Based Histograms

Yossi Matias;Jeffrey Scott Vitter;Min Wang.
very large data bases (2000)

248 Citations

Linking named entities in Tweets with knowledge base via user interest modeling

Wei Shen;Jianyong Wang;Ping Luo;Min Wang.
knowledge discovery and data mining (2013)

175 Citations

Efficient multi-way theta-join processing using MapReduce

Xiaofei Zhang;Lei Chen;Min Wang.
very large data bases (2012)

132 Citations

XPathLearner: an on-line self-tuning Markov histogram for XML path selectivity estimation

Lipyeow Lim;Min Wang;Sriram Padmanabhan;Jeffrey Scott Vitter.
very large data bases (2002)

122 Citations

App recommendation: a contest between satisfaction and temptation

Peifeng Yin;Ping Luo;Wang-Chien Lee;Min Wang.
web search and data mining (2013)

122 Citations

Relational database management encryption system

Jingmin He;Sriram Padmanabhan;Min Wang.
(2001)

117 Citations

Best Scientists Citing Min Wang

Anand Srinivasan

Anand Srinivasan

Business International Corporation

Publications: 39

Minos Garofalakis

Minos Garofalakis

Technical University of Crete

Publications: 32

Jiawei Han

Jiawei Han

University of Illinois at Urbana-Champaign

Publications: 29

Alfredo Cuzzocrea

Alfredo Cuzzocrea

University of Calabria

Publications: 27

Surajit Chaudhuri

Surajit Chaudhuri

Microsoft (United States)

Publications: 24

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 23

Nick Koudas

Nick Koudas

University of Toronto

Publications: 21

Sudipto Guha

Sudipto Guha

University of Pennsylvania

Publications: 21

Charu C. Aggarwal

Charu C. Aggarwal

IBM (United States)

Publications: 21

Shailendra Mishra

Shailendra Mishra

Business International Corporation

Publications: 20

Domenico Saccà

Domenico Saccà

University of Calabria

Publications: 18

Yossi Matias

Yossi Matias

Google (United States)

Publications: 17

Namit Jain

Namit Jain

Business International Corporation

Publications: 15

Lei Chen

Lei Chen

Hong Kong University of Science and Technology

Publications: 15

Aoying Zhou

Aoying Zhou

East China Normal University

Publications: 15

Hosagrahar V. Jagadish

Hosagrahar V. Jagadish

University of Michigan–Ann Arbor

Publications: 14

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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