What is he best known for?
The fields of study he is best known for:
- Database
- Programming language
- Operating system
His primary areas of study are Database, Bioinformatics, Distributed computing, Information retrieval and Web service.
His studies in Database integrate themes in fields like Interface and Implementation.
The various areas that Norman W. Paton examines in his Bioinformatics study include Ontology, Description logic, Knowledge base, Knowledge representation and reasoning and Data science.
Norman W. Paton combines subjects such as Standardization and Proteomics Standards Initiative with his study of Data science.
His Standardization research incorporates elements of Metadata and Proteomics, Human proteome project.
His Information retrieval research is multidisciplinary, incorporating perspectives in Software, Artificial intelligence and Identification.
His most cited work include:
- A common open representation of mass spectrometry data and its application to proteomics research (656 citations)
- The minimum information about a proteomics experiment (MIAPE) (608 citations)
- The minimum information about a proteomics experiment (MIAPE) (608 citations)
What are the main themes of his work throughout his whole career to date?
Norman W. Paton mostly deals with Database, Information retrieval, Data science, Programming language and Data integration.
His Database study combines topics in areas such as Object, Grid computing and Distributed computing.
As part of one scientific family, he deals mainly with the area of Information retrieval, narrowing it down to issues related to the Data modeling, and often Data model.
His work deals with themes such as Variety, Workflow and Bioinformatics, which intersect with Data science.
His work carried out in the field of Programming language brings together such families of science as Query language, Database schema and Database theory.
His Query language research focuses on subjects like Web search query, which are linked to Query optimization.
He most often published in these fields:
- Database (21.29%)
- Information retrieval (17.65%)
- Data science (13.73%)
What were the highlights of his more recent work (between 2012-2021)?
- Data integration (12.32%)
- Information retrieval (17.65%)
- Data mining (11.20%)
In recent papers he was focusing on the following fields of study:
His main research concerns Data integration, Information retrieval, Data mining, Data science and Artificial intelligence.
He has included themes like Crowdsourcing, Correctness, End user and Cluster analysis in his Data integration study.
His Information retrieval research includes themes of Set, Selection and User requirements document.
His Data mining research is multidisciplinary, incorporating elements of Construct and Search engine indexing.
His study in Data science is interdisciplinary in nature, drawing from both Variety, Data management, Data extraction and Big data.
His Human–computer interaction research incorporates elements of Ontology-based data integration, World Wide Web and Data structure.
Between 2012 and 2021, his most popular works were:
- A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes. (85 citations)
- Optimizing virtual machine placement for energy and SLA in clouds using utility functions (39 citations)
- Data Wrangling for Big Data: Challenges and Opportunities (35 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Database
- Operating system
His primary areas of investigation include Information retrieval, Data integration, Data science, End user and Data mining.
His Information retrieval study combines topics in areas such as Hierarchical clustering, Heuristics, Correctness and Selection.
His research investigates the connection between Data integration and topics such as Precision and recall that intersect with problems in User requirements document, Dataspaces, Schema, Task and Synthetic data.
Norman W. Paton integrates several fields in his works, including Data science and Skill sets.
His End user research includes elements of Theoretical computer science and Inference.
His Data mining research is multidisciplinary, incorporating perspectives in Tuple and Data discovery.
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