What is he best known for?
The fields of study he is best known for:
- Database
- Artificial intelligence
- Programming language
Bernhard Seeger mainly focuses on Theoretical computer science, Joins, Algorithm, Data set and Data mining.
Bernhard Seeger interconnects Testbed, M-tree and External storage in the investigation of issues within Theoretical computer science.
His studies deal with areas such as Tree, Range query and Heuristic as well as M-tree.
His iDistance research extends to Algorithm, which is thematically connected.
His work deals with themes such as Asymptotically optimal algorithm, Spatial database and B-tree, which intersect with Data set.
His research in the fields of Skyline, Nearest neighbor search and Skyline computation overlaps with other disciplines such as Simple.
His most cited work include:
- The R*-tree: an efficient and robust access method for points and rectangles (3895 citations)
- Progressive skyline computation in database systems (756 citations)
- An optimal and progressive algorithm for skyline queries (727 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Data mining, Theoretical computer science, Algorithm, Distributed computing and Data stream mining.
His Data mining research is multidisciplinary, incorporating perspectives in Search engine indexing and Statistical model.
Bernhard Seeger usually deals with Theoretical computer science and limits it to topics linked to Spatial analysis and Visualization.
His Algorithm research integrates issues from Set, Joins and R-tree.
His research in Distributed computing intersects with topics in Event, Complex event processing and Real-time computing.
Bernhard Seeger interconnects Query optimization and Data analysis in the investigation of issues within Data stream mining.
He most often published in these fields:
- Data mining (27.46%)
- Theoretical computer science (19.01%)
- Algorithm (14.08%)
What were the highlights of his more recent work (between 2016-2020)?
- Herbarium (4.23%)
- Complex event processing (8.45%)
- Artificial intelligence (6.34%)
In recent papers he was focusing on the following fields of study:
Bernhard Seeger spends much of his time researching Herbarium, Complex event processing, Artificial intelligence, Convolutional neural network and Data science.
His Complex event processing research incorporates elements of User space, Software, Latency and Data mining.
His study looks at the relationship between Latency and fields such as Timestamp, as well as how they intersect with chemical problems.
His studies link Latency with Algorithm.
His work on Analytics as part of general Data mining research is often related to Distribution, thus linking different fields of science.
His studies deal with areas such as Workflow and Pattern recognition as well as Artificial intelligence.
Between 2016 and 2020, his most popular works were:
- Fast Cloud Segmentation Using Convolutional Neural Networks (33 citations)
- Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks (26 citations)
- ChronicleDB: A High-Performance Event Store. (9 citations)
In his most recent research, the most cited papers focused on:
- Database
- Artificial intelligence
- Programming language
His primary areas of investigation include Real-time computing, Event, Convolutional neural network, Latency and Pattern matching.
His Real-time computing study which covers Leverage that intersects with Complex event processing.
He has included themes like Kernel and Sensor hub in his Complex event processing study.
His Convolutional neural network research is multidisciplinary, incorporating perspectives in Segmentation and Trait.
The Latency study combines topics in areas such as Timestamp, Event, Feature and Latency.
As part of his studies on Event, Bernhard Seeger frequently links adjacent subjects like Algorithm.
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