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A Framework to Benchmark NoSQL Data Stores for Large-Scale Model Persistence

  • Seyyed M. Shah
  • Ran Wei
  • Dimitrios S. Kolovos
  • Louis M. Rose
  • Richard F. Paige
  • Konstantinos Barmpis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8767)

Abstract

We present a framework and methodology to benchmark NoSQL stores for large scale model persistence. NoSQL technologies potentially improve performance of some applications and provide schema-less data-structures, so are particularly suited to persisting large and heterogeneous models. Recent studies consider only a narrow set of NoSQL stores for large scale modelling. Benchmarking many technologies requires substantial effort due to the disparate interface each store provides. Our experiments compare a broad range of NoSQL stores in terms of processor time and disc space used. The framework and methodology is evaluated through a case study that involves persisting large reverse-engineered models of open source projects. The results give tool engineers and practitioners a basis for selecting a store to persist large models.

Keywords

Large Model Property Graph Open Source Project Graph Database Model Drive Engineer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Seyyed M. Shah
    • 1
  • Ran Wei
    • 1
  • Dimitrios S. Kolovos
    • 1
  • Louis M. Rose
    • 1
  • Richard F. Paige
    • 1
  • Konstantinos Barmpis
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkUK

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