Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Fabian Hueske"'
Autor:
Slava Chernyak, Daniel Mills, Fabian Hueske, Kenneth Knowles, Kathryn Knight, Dan Sotolongo, Tyler Akidau, Edmon Begoli
Publikováno v:
Proceedings of the VLDB Endowment. 14:3135-3147
Streaming data processing is an exercise in taming disorder: from oftentimes huge torrents of information, we hope to extract powerful and timely analyses. But when dealing with streaming data, the unbounded and temporally disordered nature of real-w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::14c0b16732d585d1da2ba204706f2acc
https://doi.org/10.14778/3476311.3476389
https://doi.org/10.14778/3476311.3476389
Publikováno v:
SIGMOD Conference
Real-time data analysis and management are increasingly critical for today's businesses. SQL is the de facto lingua franca for these endeavors, yet support for robust streaming analysis and management with SQL remains limited. Many approaches restric
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ffebcf10ff6bda110869adc0a518bb42
https://doi.org/10.1145/3299869.3314040
https://doi.org/10.1145/3299869.3314040
Autor:
Fabian Hueske, Vasiliki Kalavri
Get started with Apache Flink, the open source framework that powers some of the world's largest stream processing applications. With this practical book, you'll explore the fundamental concepts of parallel stream processing and discover how this tec
Autor:
Fabian Hueske, Timo Walther
Publikováno v:
Encyclopedia of Big Data Technologies ISBN: 9783319639628
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::abea2489c0c3424a966dbdab0f1fda5d
https://doi.org/10.1007/978-3-319-63962-8_303-1
https://doi.org/10.1007/978-3-319-63962-8_303-1
Publikováno v:
Information Systems. 52:96-125
Recent years have seen an increased interest in large-scale analytical data flows on non-relational data. These data flows are compiled into execution graphs scheduled on large compute clusters. In many novel application areas the predominant buildin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d964aa0854894d059498be07b4aef02e
https://doi.org/10.1016/j.is.2015.04.002
https://doi.org/10.1016/j.is.2015.04.002
Autor:
Rico Bergmann, Alexander Alexandrov, Ulf Leser, Volker Markl, Johann-Christoph Freytag, Matthias J. Sax, Mareike Hoger, Felix Naumann, Stephan Ewen, Mathias Peters, Arvid Heise, Astrid Rheinländer, Marcus Leich, Kostas Tzoumas, Fabian Hueske, Sebastian Schelter, Odej Kao, Daniel Warneke
Publikováno v:
The VLDB Journal. 23:939-964
We present Stratosphere, an open-source software stack for parallel data analysis. Stratosphere brings together a unique set of features that allow the expressive, easy, and efficient programming of analytical applications at very large scale. Strato
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7309fe56e002403016c9880d02a74d38
https://doi.org/10.1007/s00778-014-0357-y
https://doi.org/10.1007/s00778-014-0357-y
Autor:
Stephan Ewen, Max Heimel, Volker Markl, Dominic Battré, Odej Kao, Daniel Warneke, Fabian Hueske, Erik Nijkamp, Alexander Alexandrov
Publikováno v:
Proceedings of the VLDB Endowment. 3:1625-1628
Large-scale data analysis applications require processing and analyzing of Terabytes or even Petabytes of data, particularly in the areas of web analysis or scientific data management. This trend has been discussed as "web-scale data management" in a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cce0bbc116f48020b8cd8c020161bab5
https://doi.org/10.14778/1920841.1921056
https://doi.org/10.14778/1920841.1921056
Autor:
Fabian Hueske, Volker Markl
Publikováno v:
Large-Scale Data Analytics ISBN: 9781461492412
Large-Scale Data Analytics
Large-Scale Data Analytics
Massively parallel data analysis is an emerging research topic that is motivated by the continuous growth of data sets and the rising complexity of data analysis tasks. To facilitate the analysis of big data, several parallel data processing framewor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5850f7cc61fc7982f6f045ae47e22ca0
https://doi.org/10.1007/978-1-4614-9242-9_2
https://doi.org/10.1007/978-1-4614-9242-9_2
Autor:
Volker Markl, Johann-Christoph Freytag, M. Ringwald, Kostas Tzoumas, M. Peters, Fabian Hueske, Aljoscha Krettek
Publikováno v:
ICDE
Data flows are a popular abstraction to define dataintensive processing tasks. In order to support a wide range of use cases, many data processing systems feature MapReduce-style user-defined functions (UDFs). In contrast to UDFs as known from relati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::69114062d9b5ce735fb14d41d09c378d
https://doi.org/10.1109/icde.2013.6544927
https://doi.org/10.1109/icde.2013.6544927
Autor:
Matthias J. Sax, Astrid Rheinländer, Kostas Tzoumas, Mathias Peters, Aljoscha Krettek, Fabian Hueske, Rico Bergmann
Many systems for big data analytics employ a data flow abstraction to define parallel data processing tasks. In this setting, custom operations expressed as user-defined functions are very common. We address the problem of performing data flow optimi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a45284c734b19e0797f4ee6bce1dba56
http://arxiv.org/abs/1208.0087
http://arxiv.org/abs/1208.0087