World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
58
Citations
12660
World Ranking
3650
National Ranking
163

Research.com Recognitions

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

Overview

Volker Markl is affiliated with the Technical University of Berlin in Germany. Their research primarily spans the field of Computer Science, with extensive work in related subfields such as Computer Networks and Communications, Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, and Signal Processing.

The main topics of their research include Advanced Database Systems and Queries, Cloud Computing and Resource Management, Data Stream Mining Techniques, Data Management and Algorithms, Parallel Computing and Optimization Techniques, Advanced Data Storage Technologies, and Scientific Computing and Data Management.

Volker Markl has published numerous papers in several frequent venues, including:

  • Proceedings of the VLDB Endowment
  • arXiv (Cornell University)
  • ACM SIGMOD Record
  • Proceedings of the ACM on Management of Data
  • The VLDB Journal

Selected recent papers authored or co-authored by Volker Markl include:

  • The Seattle Report on Database Research, 2020, ACM SIGMOD Record
  • Continuous Training and Deployment of Deep Learning Models, 2021, Datenbank-Spektrum
  • Query Processing on Heterogeneous CPU/GPU Systems, 2022, ACM Computing Surveys
  • Artificial intelligence to advance Earth observation: A review of models, recent trends, and pathways forward, 2023, arXiv (Cornell University)
  • The Seattle report on database research, 2022, Communications of the ACM

Frequent collaborators in their research include Jorge-Arnulfo Quiané-Ruiz, Zoi Kaoudi, Steffen Zeuch, Eleni Tzirita Zacharatou, and Tilmann Rabl.

In recognition of their contributions, Volker Markl was named an ACM Fellow in 2020 for work related to query optimization, scalable data processing, and data programmability.

Best Publications

  • Apache flink : Stream and batch processing in a single engine

    Paris Carbone;Paris Carbone;Asterios Katsifodimos;Asterios Katsifodimos;Stephan Ewen;Volker Markl;Volker Markl

  • The Stratosphere platform for big data analytics

    Alexander Alexandrov;Rico Bergmann;Stephan Ewen;Johann-Christoph Freytag

  • LEO - DB2's LEarning Optimizer

    Michael Stillger;Guy M. Lohman;Volker Markl;Mokhtar Kandil

  • Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding

    Gencer Sumbul;Marcela Charfuelan;Begum Demir;Volker Markl

  • CORDS: automatic discovery of correlations and soft functional dependencies

    Ihab F. Ilyas;Volker Markl;Peter Haas;Paul Brown

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

    Dominic Battré;Stephan Ewen;Fabian Hueske;Odej Kao

  • Robust query processing through progressive optimization

    Volker Markl;Vijayshankar Raman;David Simmen;Guy Lohman

  • Benchmarking Distributed Stream Data Processing Systems

    Jeyhun Karimov;Tilmann Rabl;Asterios Katsifodimos;Roman Samarev

  • Integrating the UB-Tree into a Database System Kernel

    Frank Ramsak;Volker Markl;Robert Fenk;Martin Zirkel

  • The Beckman report on database research

    Daniel Abadi;Rakesh Agrawal;Anastasia Ailamaki;Magdalena Balazinska

  • Query Expansion

    Unknown

  • Hardware-oblivious parallelism for in-memory column-stores

    Max Heimel;Michael Saecker;Holger Pirk;Stefan Manegold

  • BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval.

    Gencer Sumbul;Arne de Wall;Tristan Kreuziger;Filipe Marcelino

  • LEO: An autonomic query optimizer for DB2

    V. Markl;G. M. Lohman;V. Raman

  • Learning from empirical results in query optimization

    Guy Maring Lohman;Michael Stillger;Volker Markl

  • Damia: data mashups for intranet applications

    David E. Simmen;Mehmet Altinel;Volker Markl;Sriram Padmanabhan

  • Damia: a data mashup fabric for intranet applications

    Mehmet Altinel;Paul Brown;Susan Cline;Rajesh Kartha

  • Automatically and adaptively determining execution plans for queries with parameter markers

    Wei Fan;Guy Maring Lohman;Volker Gerhard Markl;Nimrod Megiddo

  • Improving OLAP performance by multidimensional hierarchical clustering

    V. Markl;F. Ramsak;R. Bayer

  • Progressive refinement of a federated query plan during query execution

    Stephan Eberhard Ewen;Holger Kache;Volker Gerhard Markl;Vijayshankar Raman

  • Spinning fast iterative data flows

    Stephan Ewen;Kostas Tzoumas;Moritz Kaufmann;Volker Markl

  • Big Data: Eine interdisziplinäre Chance für die Wirtschaftsinformatik

    Michael Schermann;Holmer Hemsen;Christoph Buchmüller;Till Bitter

Frequent Co-Authors

Peter J. Haas
Peter J. Haas University of Massachusetts Amherst
Guy M. Lohman
Guy M. Lohman IBM (United States)
Vijayshankar Raman
Vijayshankar Raman Google (United States)
Nimrod Megiddo
Nimrod Megiddo IBM (United States)
Ashraf Aboulnaga
Ashraf Aboulnaga The University of Texas at Arlington
Donald Kossmann
Donald Kossmann Microsoft (United States)
Ihab F. Ilyas
Ihab F. Ilyas University of Waterloo
Seif Haridi
Seif Haridi Royal Institute of Technology
Sam Lightstone
Sam Lightstone IBM (United States)
Gunter Saake
Gunter Saake Otto-von-Guericke University Magdeburg

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