2014 - Member of Academia Europaea
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 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.
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.
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.
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.
SAP HANA database: data management for modern business applications
Franz Färber;Sang Kyun Cha;Jürgen Primsch;Christof Bornhövd.
international conference on management of data (2012)
The SAP HANA Database - An Architecture Overview
Franz Färber;Norman May;Wolfgang Lehner;Philipp Große.
IEEE Data(base) Engineering Bulletin (2012)
Modelling Large Scale OLAP Scenarios
Wolfgang Lehner.
extending database technology (1998)
Efficient transaction processing in SAP HANA database: the end of a column store myth
Vishal Sikka;Franz Färber;Wolfgang Lehner;Sang Kyun Cha.
international conference on management of data (2012)
Normal forms for multidimensional databases
W. Lehner;J. Albrecht;H. Wedekind.
statistical and scientific database management (1998)
FPTree: A Hybrid SCM-DRAM Persistent and Concurrent B-Tree for Storage Class Memory
Ismail Oukid;Johan Lasperas;Anisoara Nica;Thomas Willhalm.
international conference on management of data (2016)
Euro-Par 2006 Parallel Processing
Wolfgang Lehner;Norbert Meyer;Achim Streit;Craig Stewart.
(2006)
Efficient exploitation of similar subexpressions for query processing
Jingren Zhou;Per-Ake Larson;Johann-Christoph Freytag;Wolfgang Lehner.
international conference on management of data (2007)
Representing Data Quality in Sensor Data Streaming Environments
A. Klein;W. Lehner.
Journal of Data and Information Quality (2009)
RiTE: Providing On-Demand Data for Right-Time Data Warehousing
C. Thomsen;T.B. Pedersen;W. Lehner.
international conference on data engineering (2008)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
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