Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Patrick Damme"'
Autor:
Patrick Damme, Marius Birkenbach, Constantinos Bitsakos, Matthias Boehm, Philippe Bonnet, Florina Ciorba, Mark Dokter, Pawel Dowgiallo, Ahmed Eleliemy, Christian Faerber, Georgios Goumas, Dirk Habich, Niclas Hedam, Marlies Hofer, Wenjun Huang, Kevin Innerebner, Vasileios Karakostas, Roman Kern, Tomaž Kosar, Daniel Krems, Andreas Laber, Wolfgang Lehner, Eric Mier, Marcus Paradies, Bernhard Peischl, Gabrielle Poerwawinata, Stratos Psomadakis, Tilmann Rabl, Piotr Ratuszniak, Pedro Silva, Nikolai Skuppin, Andreas Starzacher, Benjamin Steinwender, Ilin Tolovski, Pınar Tözün, Wojciech Ulatowski, Yuanyuan Wang, Izajasz Wrosz, Aleš Zamuda, Ce Zhang, Xiao Xiang Zhu, Alexander Krause
Publikováno v:
Damme, P, Birkenbach, M, Bitsakos, C, Boehm, M, Bonnet, P, Ciorba, F, Dokter, M, Dowgiallo, P, Eleliemy, A, Faerber, C, Goumas, G, Habich, D, Hedam, N, Hofer, M, Huang, W, Innerebner, K, Karakostas, V, Kern, R, Kosar, T, Krause, A, Krems, D, Laber, A, Lehner, W, Mier, E, Paradies, M, Peischl, B, Poerwawinata, G, Psomadakis, S, Rabl, T, Ratuszniak, P, Silva, P, Skuppin, N, Starzacher, A, Steinwender, B, Tolovski, I, Tözün, P, Ulatowski, W, Wang, Y, Wrosz, I, Zamuda, A, Zhang, C & Zhu, X X 2022, DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines . in Conference on Innovative Data Systems Research . Santa Cruz, California, USA . < http://cidrdb.org/cidr2022/papers/p4-damme.pdf >
Benjamin Steinwender
Benjamin Steinwender
Integrated data analysis (IDA) pipelines---that combine data management (DM) and query processing, high-performance computing (HPC), and machine learning (ML) training and scoring---become increasingly common in practice. Interestingly, systems of th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::ef97c54fb73d8eceb46f77197e7fe80d
https://pure.itu.dk/ws/files/86467917/CIDR2022.pdf
https://pure.itu.dk/ws/files/86467917/CIDR2022.pdf
Publikováno v:
Datenbank-Spektrum. 19:183-197
In-memory column-store database systems are state of the art for the efficient processing of analytical workloads. In these systems, data compression as well as vectorization play an important role. Currently, the vectorized processing is done using
Publikováno v:
SYSTOR
Hybrid memory systems consisting of DRAM and NVRAM offer a great opportunity for column-oriented data systems to persistently store and to efficiently process columnar data completely in main memory. While vectorization (SIMD) of query operators is s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6d7d44d8095e27fc8b42eb9f2dfcdc6
https://tud.qucosa.de/id/qucosa:76671
https://tud.qucosa.de/id/qucosa:76671
Publikováno v:
SYSTOR
In-memory database systems adopting a columnar storage model play a crucial role with respect to data analytics. While data is completely kept in-memory by these systems for efficiency, data has to be stored on a non-volatile medium for persistence a
Autor:
Wolfgang Lehner, Dirk Habich, Annett Ungethüm, Patrick Damme, Alexander Krause, Johannes Pietrzyk
In this paper, we present MorphStore, an open-source in-memory columnar analytical query engine with a novel holistic compression-enabled processing model. Basically, compression using lightweight integer compression algorithms already plays an impor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::228e0653ae80064812346c834d40d7b7
http://arxiv.org/abs/2004.09350
http://arxiv.org/abs/2004.09350
Autor:
Wolfgang Lehner, Dirk Habich, Patrick Damme, Mikhail Zarubin, Thomas Willhalm, Thomas Kissinger
Publikováno v:
DaMoN
Lightweight integer compression algorithms play an important role in in-memory database systems to tackle the growing gap between processor speed and main memory bandwidth. Thus, there is a large number of algorithms to choose from, while different a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::725e91d3fcfcdcb11bed6ceb1548e2ed
https://tud.qucosa.de/id/qucosa:80547
https://tud.qucosa.de/id/qucosa:80547
Lightweight integer compression algorithms are frequently applied in in-memory database systems to tackle the growing gap between processor speed and main memory bandwidth. In recent years, the vectorization of basic techniques such as delta coding a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a0a8bdce6170c88583847d7edc523a0e
https://tud.qucosa.de/id/qucosa:81377
https://tud.qucosa.de/id/qucosa:81377
Autor:
Juliana Hildebrandt, Patrick Damme, Wolfgang Lehner, Dirk Habich, Johannes Pietrzyk, Annett Ungethüm, Alexander Krause
Publikováno v:
SIGMOD Conference
In this demo, we present MorphStore, an in-memory column store with a novel compression-aware query processing concept. Basically, compression using lightweight integer compression algorithms already plays an important role in existing in-memory colu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0cd5d206cd470d0db4de36458caac9e9
https://tud.qucosa.de/id/qucosa:80634
https://tud.qucosa.de/id/qucosa:80634
Publikováno v:
DBTest@SIGMOD
The exploitation of data as well as hardware properties is a core aspect for efficient data management. This holds in particular for the field of in-memory data processing. Aside from increasing main memory capacities, in-memory data processing also
Publikováno v:
ICDE Workshops
Data as well as hardware characteristics are two key aspects for efficient data management. This holds in particular for the field of in-memory data processing. Aside from increasing main memory capacities, efficient in-memory processing benefits fro