What is he best known for?
The fields of study he is best known for:
- Operating system
- Database
- Programming language
Wolfgang Lehner mostly deals with Data mining, Database, Data warehouse, Query optimization and Online analytical processing.
His Data mining research is multidisciplinary, relying on both Data modeling, Data stream and Sample.
His research integrates issues of Exploit and Data structure in his study of Database.
His Data warehouse study combines topics from a wide range of disciplines, such as Java and Architecture framework.
The various areas that Wolfgang Lehner examines in his Query optimization study include Sample size determination, Sampling, Reservoir sampling, Web query classification and Sargable.
Wolfgang Lehner combines subjects such as Curse of dimensionality and Data science with his study of Online analytical processing.
His most cited work include:
- SAP HANA database: data management for modern business applications (352 citations)
- The SAP HANA Database - An Architecture Overview (208 citations)
- Modelling Large Scale OLAP Scenarios (165 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Data mining, Database, Distributed computing, Theoretical computer science and Data warehouse.
His study in Data mining focuses on Online analytical processing in particular.
His work in Database is not limited to one particular discipline; it also encompasses Software engineering.
His studies in Theoretical computer science integrate themes in fields like Graph database, Graph and Query optimization.
His Graph database study results in a more complete grasp of Graph.
He most often published in these fields:
- Data mining (21.28%)
- Database (19.73%)
- Distributed computing (10.83%)
What were the highlights of his more recent work (between 2017-2021)?
- Parallel computing (8.90%)
- Distributed computing (10.83%)
- Data compression (4.45%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Parallel computing, Distributed computing, Data compression, Big data and Artificial intelligence.
His Distributed computing research is multidisciplinary, incorporating elements of Multi-core processor, Overhead and Set.
His Data compression course of study focuses on Speedup and Field and Instruction set.
Wolfgang Lehner interconnects Partition, Selection and Data science in the investigation of issues within Big data.
The Artificial intelligence study combines topics in areas such as Machine learning and Natural language processing.
His work in Memory footprint addresses issues such as Data mining, which are connected to fields such as Time series.
Between 2017 and 2021, his most popular works were:
- Cardinality estimation with local deep learning models (28 citations)
- Big Data Competence Center ScaDS Dresden/Leipzig: Overview and selected research activities (25 citations)
- Table Recognition in Spreadsheets via a Graph Representation (16 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Database
- Artificial intelligence
His primary areas of investigation include Data compression, Speedup, Distributed computing, Vectorization and Overhead.
His Distributed computing research incorporates themes from Energy control, In-memory database, Multi-core processor and Hardware compatibility list.
His Overhead study integrates concerns from other disciplines, such as SAP HANA, Analytics, Shared resource and Data structure.
His research in Data structure focuses on subjects like Big data, which are connected to Data management.
His Data management study combines topics in areas such as Cardinality and Machine learning.
His SIMD research includes elements of Entity–relationship model, Database design, Relational database, View and Database model.
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