Automatic Lexical Alignment between Relational Database and Heterogeneous Big Data Based on NOSQL Systems

  • Authors

    • IBN BATOUTA Zouhair
    • HAJOUI Omar
    • TALEA Mohamed
    2018-12-06
    https://doi.org/10.14419/ijet.v7i4.32.23234
  • Automatic Matching, Big Data, Columnar Store, Document Store, Generative automatic matching, Heterogeneous Meta-models, hybrid meta-heuristic, Key value Store, NoSql, Quality mathematical measurements.Relational,

  • In the absence of a universal consensus governing the use of models, the IDM has led to the creation of a large number of heterogeneous (distinct) meta-model systems with similar or complementary uses and objectives. To solve this problem of increasing heterogeneity of meta-models, we proposed an approach that we named generative automatic matching GAM. In this approach, we have dealt with the problem of the heterogeneity of meta-models in a new way that uses automatic matching, the alignments found are then took into profit to facilitate generation between source and target models which are conform to the linked meta-models.

     

    This article presents an application of our GAM approach on a case study composed of heterogeneous meta-models of relational databases and big data, we specially treat the application of lexical automatic matching based on hybrid meta-heuristic. We have selected three types of databases and big data based on NoSql: Key Store, Document Store and Columnar Store; at the end of our article we present an evaluation of the results found based on quality mathematical measurements.

     

     

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  • How to Cite

    BATOUTA Zouhair, I., Omar, H., & Mohamed, T. (2018). Automatic Lexical Alignment between Relational Database and Heterogeneous Big Data Based on NOSQL Systems. International Journal of Engineering & Technology, 7(4.32), 1-6. https://doi.org/10.14419/ijet.v7i4.32.23234