What is he best known for?
The fields of study he is best known for:
- Programming language
- Database
- Operating system
Bertram Ludäscher focuses on Workflow, Data science, XML, Information retrieval and Data management.
His work is dedicated to discovering how Workflow, Software engineering are connected with Data flow diagram and Executable and other disciplines.
In Data science, Bertram Ludäscher works on issues like Knowledge extraction, which are connected to Big data.
His XML research incorporates elements of Query language, Programming language, Theoretical computer science and Systems architecture.
His Information retrieval research incorporates themes from Scheme, Semantics, Overhead and Information integration.
His Data management research includes elements of Ontology, Data integration and Semantic data model.
His most cited work include:
- Scientific Workflow Management and the Kepler System (1465 citations)
- Scientific Workflow Management and the Kepler System (1465 citations)
- Kepler: an extensible system for design and execution of scientific workflows (709 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Workflow, Data science, Information retrieval, World Wide Web and Provenance.
His study explores the link between Workflow and topics such as Software engineering that cross with problems in Software.
The Data science study combines topics in areas such as Data integration, Data management, Knowledge extraction and Kepler scientific workflow system.
While the research belongs to areas of Information retrieval, Bertram Ludäscher spends his time largely on the problem of XML, intersecting his research to questions surrounding Query language.
In his study, Semantics and Programming language is inextricably linked to Active database, which falls within the broad field of Datalog.
His work carried out in the field of Database brings together such families of science as Graph, Dependency graph and Executable.
He most often published in these fields:
- Workflow (41.57%)
- Data science (22.35%)
- Information retrieval (18.43%)
What were the highlights of his more recent work (between 2015-2021)?
- Data science (22.35%)
- Workflow (41.57%)
- Provenance (14.51%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Data science, Workflow, Provenance, Information retrieval and Datalog.
His research in Data science is mostly concerned with Cyberinfrastructure.
His Workflow study combines topics from a wide range of disciplines, such as Data mining, Big data, Inference, Data dependency and Software.
His Big data research is multidisciplinary, incorporating perspectives in Knowledge extraction and Implementation.
His Information retrieval study integrates concerns from other disciplines, such as Taxon and Matrix.
His research on Datalog also deals with topics like
- Rewriting which connect with Graph and Factorization,
- SQL which connect with Theoretical computer science.
Between 2015 and 2021, his most popular works were:
- Computing environments for reproducibility: Capturing the “Whole Tale” (57 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:
- Programming language
- Database
- Operating system
The scientist’s investigation covers issues in Provenance, Information retrieval, Data science, Artificial intelligence and Negation.
His Data science study combines topics in areas such as Semantic interoperability, Workflow and Knowledge management.
His Workflow research includes themes of Knowledge extraction and Big data.
His research investigates the connection between Artificial intelligence and topics such as Natural language processing that intersect with issues in Ontology alignment, Pairwise comparison, Identifier and Knowledge representation and reasoning.
His research integrates issues of Rewriting, Datalog, Theoretical computer science, Graph and Debugging in his study of Negation.
He interconnects Context and Graph in the investigation of issues within Theoretical computer science.
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