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

  • Abstract
  • Keywords
  • References
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  • Abstract

    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.



  • Keywords

    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;

  • References

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Article ID: 23234
DOI: 10.14419/ijet.v7i4.32.23234

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