D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 47 Citations 10,414 206 World Ranking 3309 National Ranking 153

Research.com Recognitions

Awards & Achievements

2020 - ACM Fellow For contributions to query optimization, scalable data processing, and data programmability

Overview

What is he best known for?

The fields of study he is best known for:

  • Operating system
  • Database
  • Programming language

Volker Markl mostly deals with Query optimization, Data mining, Set, Sargable and Query plan. His Query optimization study necessitates a more in-depth grasp of Database. His Data mining research includes elements of Cardinality, Row and Predicate.

His Set research is multidisciplinary, incorporating perspectives in Domain, Debugging, Column and Parallel computing. The various areas that he examines in his Parallel computing study include Data flow diagram and State. His work carried out in the field of Query language brings together such families of science as Programming language and Scalability.

His most cited work include:

  • Apache flink : Stream and batch processing in a single engine (644 citations)
  • The Stratosphere platform for big data analytics (357 citations)
  • LEO - DB2's LEarning Optimizer (344 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of study are Data mining, Query optimization, Scalability, Distributed computing and Database. As part of his studies on Data mining, Volker Markl often connects relevant areas like Theoretical computer science. The Query optimization study combines topics in areas such as Query language and View.

His Scalability research is multidisciplinary, relying on both Data management and Parallel computing. In his research, Programming paradigm is intimately related to Cloud computing, which falls under the overarching field of Distributed computing. Volker Markl merges Database with Query plan in his research.

He most often published in these fields:

  • Data mining (22.67%)
  • Query optimization (18.22%)
  • Scalability (14.17%)

What were the highlights of his more recent work (between 2018-2021)?

  • Stream processing (7.29%)
  • Data management (9.72%)
  • Real-time computing (4.86%)

In recent papers he was focusing on the following fields of study:

Volker Markl mainly focuses on Stream processing, Data management, Real-time computing, Distributed computing and Scalability. Volker Markl has researched Stream processing in several fields, including Data stream mining, STREAMS and Code generation. His Data management research is multidisciplinary, incorporating elements of Data science, The Internet, Artificial intelligence, Internet privacy and Big data.

His Distributed computing research incorporates themes from Skew and Server. His biological study spans a wide range of topics, including Agora, Process and Provisioning. His work is dedicated to discovering how Latency, Joins are connected with Query optimization and other disciplines.

Between 2018 and 2021, his most popular works were:

  • Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding (69 citations)
  • Analyzing efficient stream processing on modern hardware (32 citations)
  • The Seattle Report on Database Research (20 citations)

In his most recent research, the most cited papers focused on:

  • Operating system
  • Database
  • Programming language

His scientific interests lie mostly in Stream processing, Real-time computing, Artificial intelligence, Relational algebra and Cloud computing. The subject of his Stream processing research is within the realm of Distributed computing. His Real-time computing study integrates concerns from other disciplines, such as Window, Sports analytics, Set and Time series.

His studies deal with areas such as Data access and Machine learning as well as Artificial intelligence. His study in Relational algebra is interdisciplinary in nature, drawing from both Data management, Wireless sensor network, Application lifecycle management, Constant and Programming paradigm. The study incorporates disciplines such as Layer, Data processing, Data science and Join in addition to Cloud computing.

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.

Best Publications

Apache flink : Stream and batch processing in a single engine

Paris Carbone;Asterios Katsifodimos;Stephan Ewen;Volker Markl.
IEEE Data(base) Engineering Bulletin (2015)

1177 Citations

The Stratosphere platform for big data analytics

Alexander Alexandrov;Rico Bergmann;Stephan Ewen;Johann-Christoph Freytag.
very large data bases (2014)

553 Citations

LEO - DB2's LEarning Optimizer

Michael Stillger;Guy M. Lohman;Volker Markl;Mokhtar Kandil.
very large data bases (2001)

459 Citations

Nephele/PACTs: a programming model and execution framework for web-scale analytical processing

Dominic Battré;Stephan Ewen;Fabian Hueske;Odej Kao.
symposium on cloud computing (2010)

379 Citations

CORDS: automatic discovery of correlations and soft functional dependencies

Ihab F. Ilyas;Volker Markl;Peter Haas;Paul Brown.
international conference on management of data (2004)

363 Citations

Robust query processing through progressive optimization

Volker Markl;Vijayshankar Raman;David Simmen;Guy Lohman.
international conference on management of data (2004)

292 Citations

Integrating the UB-Tree into a Database System Kernel

Frank Ramsak;Volker Markl;Robert Fenk;Martin Zirkel.
very large data bases (2000)

234 Citations

Learning from empirical results in query optimization

Guy Maring Lohman;Michael Stillger;Volker Markl.
(2001)

160 Citations

LEO: An autonomic query optimizer for DB2

V. Markl;G. M. Lohman;V. Raman.
Ibm Systems Journal (2003)

154 Citations

Hardware-oblivious parallelism for in-memory column-stores

Max Heimel;Michael Saecker;Holger Pirk;Stefan Manegold.
very large data bases (2013)

150 Citations

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