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Volker Markl
Researcher at Technical University of Berlin
Publications - 301
Citations - 10669
Volker Markl is an academic researcher from Technical University of Berlin. The author has contributed to research in topics: Query optimization & Computer science. The author has an hindex of 46, co-authored 258 publications receiving 9114 citations. Previous affiliations of Volker Markl include IBM & Fraunhofer Society.
Papers
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Journal ArticleDOI
Big Data: Eine interdisziplinäre Chance für die Wirtschaftsinformatik
Michael Schermann,Holmer Hemsen,Christoph Buchmüller,Till Bitter,Helmut Krcmar,Volker Markl,Thomas Hoeren +6 more
TL;DR: Information systems research is ideally positioned to support big data critically and use the knowledge gained to explain and design innovative information systems in business and administration – regardless of whether big data is in reality a disruptive technology or a cursory fad.
Journal Article
Apache flink : Stream and batch processing in a single engine
Paris Carbone,Paris Carbone,Asterios Katsifodimos,Asterios Katsifodimos,Stephan Ewen,Volker Markl,Volker Markl,Seif Haridi,Seif Haridi,Kostas Tzoumas +9 more
TL;DR: This paper discusses the approach to achieve high throughput for transactional query processing while allowing concurrent analytical queries, and presents its approach to distributed snapshot isolation and optimized two-phase commit protocols.
Journal ArticleDOI
The Stratosphere platform for big data analytics
Alexander Alexandrov,Rico Bergmann,Stephan Ewen,Johann-Christoph Freytag,Fabian Hueske,Arvid Heise,Odej Kao,Marcus Leich,Ulf Leser,Volker Markl,Felix Naumann,Mathias Peters,Astrid Rheinländer,Matthias J. Sax,Sebastian Schelter,Mareike Hoger,Kostas Tzoumas,Daniel Warneke +17 more
TL;DR: The overall system architecture design decisions are presented, Stratosphere is introduced through example queries, and the internal workings of the system’s components that relate to extensibility, programming model, optimization, and query execution are dive into.
Proceedings Article
LEO - DB2's LEarning Optimizer
TL;DR: LEO, DB2's LEarning Optimizer, is introduced as a comprehensive way to repair incorrect statistics and cardinality estimates of a query execution plan by monitoring previously executed queries and computes adjustments to cost estimates and statistics that may be used during future query optimizations.
Proceedings ArticleDOI
CORDS: automatic discovery of correlations and soft functional dependencies
TL;DR: CorDS as mentioned in this paper is an efficient and scalable tool for automatic discovery of correlations and soft functional dependencies between columns, which can be used as a data mining tool, producing dependency graphs that are of intrinsic interest.