What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Database
- Programming language
His primary scientific interests are in Workflow, Data science, XML, Information retrieval and Ontology.
Shawn Bowers has included themes like Reuse and Component in his Workflow study.
The study incorporates disciplines such as World Wide Web, Darwin Core, Set and Data discovery in addition to Data science.
His XML research is multidisciplinary, relying on both Theoretical computer science, RDF, Topic Maps and Metamodeling.
His Information retrieval research includes themes of Data integration and Ecological Metadata Language.
His Ontology research includes elements of Ecology, Biodiversity, Data mapping and Data element.
His most cited work include:
- An ontology for describing and synthesizing ecological observation data (184 citations)
- The New Bioinformatics: Integrating Ecological Data from the Gene to the Biosphere (171 citations)
- Provenance in Scientific Workflow Systems (162 citations)
What are the main themes of his work throughout his whole career to date?
Shawn Bowers mostly deals with Workflow, Data science, Information retrieval, Workflow technology and Workflow engine.
Shawn Bowers integrates Workflow and Provenance in his studies.
His Data science study also includes
- Biological data and Pipeline most often made with reference to Kepler scientific workflow system,
- Metadata that connect with fields like Ecology.
The Information retrieval study combines topics in areas such as Data mining and Database.
His study in Workflow technology is interdisciplinary in nature, drawing from both Workflow management system and Data lineage.
His research integrates issues of Programming language and Dependency graph in his study of Workflow engine.
He most often published in these fields:
- Workflow (48.76%)
- Data science (23.14%)
- Information retrieval (19.83%)
What were the highlights of his more recent work (between 2013-2018)?
- Workflow (48.76%)
- Answer set programming (4.96%)
- Data dependency (4.96%)
In recent papers he was focusing on the following fields of study:
Shawn Bowers mainly focuses on Workflow, Answer set programming, Data dependency, Data mining and Natural language processing.
Particularly relevant to Workflow engine is his body of work in Workflow.
His biological study spans a wide range of topics, including Distributed computing, Dataflow, Workflow technology, Perl and Workflow management system.
His Answer set programming research focuses on Theoretical computer science and how it connects with White box.
Shawn Bowers has researched Data mining in several fields, including Automated theorem proving and Logical consequence.
His Natural language processing study combines topics from a wide range of disciplines, such as Identifier, Scalability and Artificial intelligence.
Between 2013 and 2018, his most popular works were:
- Semantics in Support of Biodiversity Knowledge Discovery: An Introduction to the Biological Collections Ontology and Related Ontologies (87 citations)
- Names are not good enough: Reasoning over taxonomic change in the Andropogon complex (29 citations)
- Two Influential Primate Classifications Logically Aligned (26 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Database
- Programming language
His primary areas of investigation include Artificial intelligence, Congruence, Region connection calculus, Natural language processing and Workflow.
His work deals with themes such as Scalability, Species level, Information integration and Answer set programming, which intersect with Artificial intelligence.
His Scalability research is multidisciplinary, incorporating perspectives in Imperfect, Ostensive definition and Bioinformatics.
Shawn Bowers combines subjects such as Identifier, Pairwise comparison, Ontology alignment and Knowledge representation and reasoning with his study of Answer set programming.
With his scientific publications, his incorporates both Congruence and Data mining.
His work on Workflow engine as part of general Workflow study is frequently linked to Directory, therefore connecting diverse disciplines of science.
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