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Classifying Big Data Analytic Approaches: A Generic Architecture

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Software Technologies (ICSOFT 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 868))

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Abstract

The explosion of the huge amount of generated data to be analyzed by several applications, imposes the trend of the moment, the Big Data boom, which in turn causes the existence of a vast landscape of architectural solutions. Non expert users who have to decide which analytical solutions are the most appropriates for their particular constraints and specific requirements in a Big Data context, are today lost, faced with a panoply of disparate and diverse solutions. To support users in this hard selection task, in a previous work, we proposed a generic architecture to classify Big Data Analytical Approaches and a set of criteria of comparison/evaluation. In this paper, we extend our classification architecture to consider more types of Big Data analytic tools and approaches and improve the list of criteria to evaluate them. We classify different existing Big Data analytics solutions according to our proposed generic architecture and qualitatively evaluate them in terms of the criteria of comparison. Additionally, we propose a preliminary design of a decision support system, intended to generate suggestions to users based on such classification and on a qualitative evaluation in terms of previous users experiences, users requirements, nature of the analysis they need, and the set of evaluation criteria.

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Notes

  1. 1.

    http://hadoop.apache.org.

  2. 2.

    http://storm.apache.org.

  3. 3.

    https://flink.apache.org.

  4. 4.

    http://www.pentaho.com/product/data-integration.

  5. 5.

    http://www.talend.com.

  6. 6.

    http://mahout.apache.org/.

  7. 7.

    https://spark.apache.org/mllib/.

  8. 8.

    http://www.cs.waikato.ac.nz/ml/index.html.

  9. 9.

    https://www.h2o.ai/h2o/.

  10. 10.

    http://oryx.io.

  11. 11.

    https://samoa.incubator.apache.org.

  12. 12.

    http://www.cascading.org.

  13. 13.

    https://www.elastic.co/fr/products/elasticsearch.

  14. 14.

    http://lucene.apache.org/solr/.

  15. 15.

    http://kylin.apache.org.

  16. 16.

    https://www.cloudera.com/products/open-source/apache-hadoop/impala.html.

  17. 17.

    http://www.microsoft.com/azure.

  18. 18.

    http://www.asterdata.com/.

  19. 19.

    http://www.actian.com.

  20. 20.

    http://nanocubes.net.

  21. 21.

    https://help.sap.com/viewer/product/SAP_HANA_PLATFORM/2.0.00/en-US.

  22. 22.

    Apache Giraph - http://giraph.apache.org.

  23. 23.

    Graph Engine https://www.graphengine.io.

  24. 24.

    https://ignite.apache.org.

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Authors and Affiliations

  1. Dpto. de Computación y TI, Universidad Simón Bolívar, Caracas, 1080-A, Venezuela

    Yudith Cardinale

  2. Université Paris Nanterre, 92001, Nanterre, France

    Sonia Guehis & Marta Rukoz

  3. Université Paris Dauphine, PSL Research University, CNRS, UMR[7243], LAMSADE, 75016, Paris, France

    Sonia Guehis & Marta Rukoz

Authors
  1. Yudith Cardinale
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  2. Sonia Guehis
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  3. Marta Rukoz
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Correspondence to Yudith Cardinale .

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Editors and Affiliations

  1. King Juan Carlos University, Madrid, Spain

    Enrique Cabello

  2. University of Coimbra, Coimbra, Portugal

    Jorge Cardoso

  3. Wroclaw University of Economics, Wroclaw, Poland

    Leszek A. Maciaszek

  4. Computer Science, University of Twente, Enschede, The Netherlands

    Marten van Sinderen

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Cardinale, Y., Guehis, S., Rukoz, M. (2018). Classifying Big Data Analytic Approaches: A Generic Architecture. In: Cabello, E., Cardoso, J., Maciaszek, L., van Sinderen, M. (eds) Software Technologies. ICSOFT 2017. Communications in Computer and Information Science, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-319-93641-3_13

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