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BigDataStack - D5.1 WP 5 Scientific Report and Prototype Description - Y1

Authors: Amaryllis Raouzaiou; Konstantinos Giannakakis; George Kousiouris; Sophia Karagiorgou; Nikos Lykousas; Pavlos Kranas; Ricardo Manuel Pereira Vilaça; +10 Authors

BigDataStack - D5.1 WP 5 Scientific Report and Prototype Description - Y1

Abstract

{"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\u201365. 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/"]}

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.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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