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Expand your Training Limits! Generating Training Data for ML-based Data Management [29]

Francesco Ventura, Zoi Kaoudi, Jorge Arnulfo Quiane Ruiz, Volker Markl

SIGMOD 2021

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Semantification of Identifiers in Mathematics for Better Math Information Retrieval [31]

Schubotz, Moritz; Grigorev, Alexey; Leich, Marcus; Cohl, Howard S.; Meuschke, Norman; Gipp, Bela; Youssef, Abdou S.; Markl, Volker

SIGIR '16 : Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 135-144. 2016

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Benchmarking in the Cloud: What It Should, Can, and Cannot Be [33]

Enno Folkerts, Alexander Alexandrov, Kai Sachs, Alexandru Iosup, Volker Markl, Cafer Tosun

Selected Topics in Performance Evaluation and Benchmarking, Part of the Lecture Notes in Computer Science book series (LNCS), TPCTC 2012, 173-188. 2012

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Fault-Tolerance for Distributed Iterative Dataflows in Action [35]

Chen Xu, Rudi Poepsel Lemaitre, Juan Soto, Volker Markl

PVLDB 11(12), 1990-1993. 2018

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Hardware-Oblivious Parallelism for In-Memory Column-Stores [37]

Max Heimel and Michael Saecker and Holger Pirk and Stefan Manegold and Volker Markl

PVLDB, 709-720. 2013

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Scotch: Generating FPGA-Accelerators for Sketching at Line Rate [39]

Martin Kiefer, Ilias Poulakis, Sebastian Breß, Volker Markl

PVLDB 2021

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Parallelizing query optimization [41]

Wook-Shin Han and Wooseong Kwak and Jinsoo Lee and Guy M. Lohman and Volker Markl

PVLDB, 188-200. 2008

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STREAMLINE - Streamlined Analysis of Data at Rest and Data in Motion [43]

Philipp M. Grulich, Tilmann Rabl, Volker Markl, Csaba Sidló, Andras Benczur

Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference 2017

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Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models [45]

Martin Kiefer, Max Heimel, Sebastian Breß, Volker Markl

Proceedings of the VLDB Endowment, Volume 10, No. 13. 2017 (to be presented in VLDB 2018) 2017

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Spinning Fast Iterative Data Flows [47]

Stephan Ewen, Kostas Tzoumas, Moritz Kaufmann, Volker Markl

Proceedings of the VLDB Endowment (PVLDB),, 1268-1279. 2012

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Publications

S

Expand your Training Limits! Generating Training Data for ML-based Data Management [29]

Semantification of Identifiers in Mathematics for Better Math Information Retrieval [31]

Benchmarking in the Cloud: What It Should, Can, and Cannot Be [33]

P

Fault-Tolerance for Distributed Iterative Dataflows in Action [35]

Hardware-Oblivious Parallelism for In-Memory Column-Stores [37]

Scotch: Generating FPGA-Accelerators for Sketching at Line Rate [39]

Parallelizing query optimization [41]

STREAMLINE - Streamlined Analysis of Data at Rest and Data in Motion [43]

Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models [45]

Spinning Fast Iterative Data Flows [47]

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Inhalt des Dokuments

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Expand your Training Limits! Generating Training Data for ML-based Data Management [29]

Semantification of Identifiers in Mathematics for Better Math Information Retrieval [31]

Benchmarking in the Cloud: What It Should, Can, and Cannot Be [33]

P

Fault-Tolerance for Distributed Iterative Dataflows in Action [35]

Hardware-Oblivious Parallelism for In-Memory Column-Stores [37]

Scotch: Generating FPGA-Accelerators for Sketching at Line Rate [39]

Parallelizing query optimization [41]

STREAMLINE - Streamlined Analysis of Data at Rest and Data in Motion [43]

Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models [45]

Spinning Fast Iterative Data Flows [47]

Zusatzinformationen / Extras

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Auxiliary Functions