What is he best known for?
The fields of study he is best known for:
- Database
- Software
- Artificial intelligence
Juan Trujillo focuses on Data warehouse, Unified Modeling Language, Data mining, Software engineering and Database.
The study incorporates disciplines such as Data science, Conceptual model, Set and Dimensional modeling in addition to Data warehouse.
His research in the fields of Applications of UML overlaps with other disciplines such as Health care.
His Data mining study incorporates themes from Software development, Information needs and Logical conjunction.
In his research, Model-driven architecture is intimately related to Component, which falls under the overarching field of Database.
While the research belongs to areas of Modeling language, Juan Trujillo spends his time largely on the problem of Object Constraint Language, intersecting his research to questions surrounding Online analytical processing.
His most cited work include:
- A UML profile for multidimensional modeling in data warehouses (249 citations)
- Research in data warehouse modeling and design: dead or alive? (184 citations)
- A UML Based Approach for Modeling ETL Processes in Data Warehouses (172 citations)
What are the main themes of his work throughout his whole career to date?
Juan Trujillo mainly investigates Data warehouse, Unified Modeling Language, Database, Online analytical processing and Software engineering.
His Data warehouse study is concerned with the larger field of Data mining.
The various areas that he examines in his Unified Modeling Language study include Data modeling and Modeling language.
His studies deal with areas such as Common Warehouse Metamodel, Cwm, Metadata and Code as well as Database.
His Online analytical processing research is multidisciplinary, incorporating elements of Object-oriented programming, Information retrieval and Decision-making.
His research integrates issues of Business intelligence and Big data in his study of Data science.
He most often published in these fields:
- Data warehouse (64.42%)
- Unified Modeling Language (29.59%)
- Database (27.34%)
What were the highlights of his more recent work (between 2014-2021)?
- Big data (7.12%)
- Data science (16.85%)
- Data warehouse (64.42%)
In recent papers he was focusing on the following fields of study:
His main research concerns Big data, Data science, Data warehouse, Business intelligence and Performance indicator.
Juan Trujillo has researched Big data in several fields, including Data management, Risk analysis, Visualization, Data visualization and Data cube.
His study in Data science is interdisciplinary in nature, drawing from both Data modeling, Visual analytics, Data mining and Datawarehouses.
His study with Data warehouse involves better knowledge in Database.
His Database research incorporates themes from Metadata and Metamodeling.
His studies in Business intelligence integrate themes in fields like Analytics and Information needs.
Between 2014 and 2021, his most popular works were:
- Current state of Linked Data in digital libraries (37 citations)
- Tactical Business-Process-Decision Support based on KPIs Monitoring and Validation (16 citations)
- Specification and derivation of key performance indicators for business analytics (15 citations)
In his most recent research, the most cited papers focused on:
- Database
- Software
- Artificial intelligence
Juan Trujillo mostly deals with Data science, Business intelligence, Big data, Visualization and Data visualization.
He has included themes like Data modeling, Ontology, Decision tree, Knowledge acquisition and Semantics in his Data science study.
His Data modeling study combines topics in areas such as Process modeling, Information visualization, Analytics and Information needs.
His Business intelligence research includes elements of Question answering, Information retrieval, Data warehouse and Unstructured data.
Data warehouse is a subfield of Database that Juan Trujillo tackles.
His work on Creative visualization and Visual analytics as part of general Visualization study is frequently connected to Best practice, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
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