What is he best known for?
The fields of study he is best known for:
- Programming language
- Artificial intelligence
- Database
Paolo Missier mainly focuses on Workflow, World Wide Web, Provenance, Software engineering and Workflow management system.
His Workflow research incorporates elements of Data domain, Data processing, Service and Data science.
His Linked data study in the realm of World Wide Web connects with subjects such as Point.
His Provenance study spans across into areas like Database, Extension, Information retrieval and Metadata.
His Workflow management system research integrates issues from Extensibility, Programming language, Syntax, Workflow engine and Workflow technology.
His Programming language research is multidisciplinary, incorporating perspectives in Computational linguistics and Semantics.
His most cited work include:
- The Open Provenance Model core specification (v1.1) (560 citations)
- Why linked data is not enough for scientists (251 citations)
- Taverna, reloaded (125 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Workflow, World Wide Web, Data science, Provenance and Information retrieval.
His biological study spans a wide range of topics, including Cloud computing and Software engineering.
Paolo Missier conducts interdisciplinary study in the fields of Software engineering and Reuse through his research.
His Data science study integrates concerns from other disciplines, such as e-Science, Social media, Information quality and Big data.
While the research belongs to areas of Information quality, Paolo Missier spends his time largely on the problem of Data quality, intersecting his research to questions surrounding Data mining.
In his works, Paolo Missier performs multidisciplinary study on Provenance and Database.
He most often published in these fields:
- Workflow (29.10%)
- World Wide Web (20.08%)
- Data science (18.03%)
What were the highlights of his more recent work (between 2016-2021)?
- Data science (18.03%)
- Theoretical computer science (6.56%)
- Artificial intelligence (6.15%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Data science, Theoretical computer science, Artificial intelligence, Machine learning and Provenance.
His work carried out in the field of Data science brings together such families of science as Value, Social media, Metadata and Big data.
The various areas that Paolo Missier examines in his Big data study include Workflow, Data analysis and Reference architecture.
His Workflow research is multidisciplinary, relying on both Graph, Correctness, Mode, Software and Programming paradigm.
Paolo Missier integrates many fields, such as Provenance, Data modeling, Evolutionary biology, Informatics, Lineage and Information retrieval, in his works.
Paolo Missier has included themes like Software engineering, Set and Pruning in his Scalability study.
Between 2016 and 2021, his most popular works were:
- Mind my value: a decentralized infrastructure for fair and trusted IoT data trading (27 citations)
- Targeted therapies for congenital myasthenic syndromes: systematic review and steps towards a treatabolome (21 citations)
- VazaDengue: An information system for preventing and combating mosquito-borne diseases with social networks (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Operating system
Paolo Missier focuses on Theoretical computer science, Computer security, Machine learning, Artificial intelligence and Data structure.
His Theoretical computer science research is multidisciplinary, incorporating elements of Simple, Rule of inference and Benchmark.
His work on Smart contract as part of general Computer security research is often related to Revenue and Flourishing, thus linking different fields of science.
His Machine learning research spans across into fields like Observational study, Medical history, Pneumonia, Mechanical ventilation and Respiratory failure.
His research in the fields of Deep learning and Deep neural networks overlaps with other disciplines such as Culex and Aedes aegypti.
The Data structure study combines topics in areas such as Python, Hash function and Graph database.
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