What is he best known for?
The fields of study he is best known for:
- Database
- Operating system
- Programming language
Database, Scalability, Artificial intelligence, Machine learning and Relational database management system are his primary areas of study.
He interconnects Query expansion, Graph and Online aggregation, Sargable in the investigation of issues within Database.
His studies examine the connections between Scalability and genetics, as well as such issues in Joins, with regards to XPath, Database design, Ranking and Aggregate data.
His research in Artificial intelligence intersects with topics in Programming language, Block and Execution plan.
His Machine learning research includes elements of Statement and Theoretical computer science.
His research investigates the connection with Relational database management system and areas like XML database which intersect with concerns in Relational database, XML framework and Document type definition.
His most cited work include:
- Efficiently Publishing Relational Data as XML Documents (324 citations)
- SystemML: Declarative machine learning on MapReduce (270 citations)
- System and method for adaptive database caching (176 citations)
What are the main themes of his work throughout his whole career to date?
Berthold Reinwald mostly deals with Artificial intelligence, Machine learning, Database, Information retrieval and Algorithm.
His Artificial intelligence research includes themes of Analytics, Computation and Operator.
His Machine learning study combines topics from a wide range of disciplines, such as Programming language and Execution plan.
As a member of one scientific family, he mostly works in the field of Database, focusing on World Wide Web and, on occasion, Data warehouse.
His Information retrieval study also includes fields such as
- Set and Search engine indexing most often made with reference to Data mining,
- Online analytical processing that connect with fields like Metadata, Aggregate and Query language.
His Relational database course of study focuses on XML and Scalability.
He most often published in these fields:
- Artificial intelligence (30.77%)
- Machine learning (29.67%)
- Database (26.37%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (30.77%)
- Machine learning (29.67%)
- Linear algebra (7.69%)
In recent papers he was focusing on the following fields of study:
Berthold Reinwald spends much of his time researching Artificial intelligence, Machine learning, Linear algebra, Theoretical computer science and Scale.
His Artificial intelligence study combines topics in areas such as Software deployment and Distributed computing.
His Machine learning research integrates issues from End-to-end principle and Online database.
His Linear algebra research includes elements of Algorithm, Lossless compression, Uncompressed video, Data compression and Column.
His Theoretical computer science study integrates concerns from other disciplines, such as Relation and Knowledge graph.
His Variety research incorporates elements of Debugging, Data integration and Data science.
Between 2017 and 2021, his most popular works were:
- On optimizing operator fusion plans for large-scale machine learning in systemML (9 citations)
- SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle (5 citations)
- SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle (4 citations)
In his most recent research, the most cited papers focused on:
- Database
- Operating system
- Programming language
His primary areas of investigation include Artificial intelligence, Machine learning, Linear algebra, Data science and Declarative programming.
In his research, Berthold Reinwald undertakes multidisciplinary study on Artificial intelligence and Scale.
Berthold Reinwald combines Machine learning and Fusion in his research.
His Linear algebra studies intersect with other disciplines such as Key, Focus, Algorithm, Benchmark and Sparse matrix.
His work deals with themes such as Variety, Debugging and Data integration, which intersect with Data science.
You can notice a mix of various disciplines of study, such as End-to-end principle and Process, in his Declarative programming studies.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
