Published August 28, 2020
| Version v1
Project deliverable
Open
BigDataStack - D5.1 WP 5 Scientific Report and Prototype Description - Y1
Creators
- Amaryllis Raouzaiou1
-
Konstantinos Giannakakis1
-
George Kousiouris2
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Sophia Karagiorgou3
-
Nikos Lykousas3
- Pavlos Kranas4
-
Ricardo Manuel Pereira Vilaça4
- Jacob Roldan4
- Francisco Ballesteros4
- Patricio Martinez4
-
Christos Doulkeridis2
- Peter Jason Gould2
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Ismael Cuadrado Cordero5
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Orlando Avila-García5
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Marta Patiño6
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Richard McCreadie7
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Stathis Plitsos8
- 1. ATC
- 2. UPRC
- 3. UBI
- 4. LXS
- 5. ATOS
- 6. UPM
- 7. GLA
- 8. DANAOS
Description
BigDataStack delivers a complete high-performant stack of technologies addressing the needs of data operations and applications. BigDataStack’s holistic solution incorporates approaches for data-focused application analysis and dimensioning, and process modelling towards increased performance, agility and efficiency. A toolkit allowing the specification of analytics tasks in a declarative way, their integration in the data path, as well as an adaptive visualization environment, realize BigDataStack’s vision of openness and extensibility.
Files
BigDataStack_D5.1_v1.0 (1).pdf
Files
(3.4 MB)
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Additional details
References
- https://nodered.org/
- https://github.com/node-red/node-red
- X. Tian et al., "BigDataBench-S: An Open-Source Scientific Big Data Benchmark Suite," 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Lake Buena Vista, FL, 2017, pp. 1068-1077.
- Ivanov et al., "Big Data Benchmark Compendium", Performance Evaluation and Benchmarking: Traditional to Big Data to Internet of Things, Springer International Publishing, 2016, pp. 135-155.
- https://www.cs.waikato.ac.nz/ml/weka/
- https://spark.apache.org/mllib/
- https://www.gnu.org/software/octave/
- https://www.ansible.com/
- https://www.dropwizard.io/
- Pavel Brazdil, Christophe G. Giraud-Carrier, Carlos Soares, Ricardo Vilalta: Metalearning - Applications to Data Mining. Cognitive Technologies, Springer 2009, ISBN 978-3-540- 73262-4, pp. I-X, 1-176
- METAL: A meta-learning assistant for providing user support in machine learning and data mining. ESPRIT Framework IV LTR Reactive Project Nr. 26.357, 1998-2001. http://www.metal-kdd.org.
- K. Morik and M. Scholz. The MiningMart approach to knowledge discovery in databases. In N. Zhong and J. Liu, editors, Intelligent Technologies for Information Analysis, chapter 3, pages 47–65. Springer, 2004. Available from http://www-ai.cs.unidortmund.de/MMWEB.
- Kate Smith-Miles: Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. 41(1): 6:1-6:25 (2008).
- Mustafa V. Nural, Hao Peng, John A. Miller: Using meta-learning for model type selection in predictive big data analytics. BigData 2017: 2027-2036.
- Daniel Gomes Ferrari, Leandro Nunes de Castro: Clustering algorithm selection by metalearning systems: A new distance-based problem characterization and ranking combination methods. Inf. Sci. 301: 181-194 (2015).
- https://d3js.org/
- https://www.highcharts.com/
- https://www.chartjs.org/