Map-Based Transparent Persistence for Very Large Models

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• 作者：Abel G贸mez (15)
  Massimo Tisi (15)
  Gerson Suny茅 (15)
  Jordi Cabot (15)
  
  15. AtlanMod Team ; Inria ; Mines Nantes ; LINA ; Nantes ; France
  
• 关键词：Model Driven Engineering ; Model Persistence ; Very Large Models ; Key ; Value Stores
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9033
• 期：1
• 页码：19-34
• 全文大小：277 KB
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  19. Espinazo Pag谩n, J., S谩nchez Cuadrado, J., Garc铆a Molina, J. Morsa: A Scalable Approach for Persisting and Accessing Large Models. In: Whittle, J., Clark, T., K眉hne, T. eds. (2011) Model Driven Engineering Languages and Systems. Springer, Heidelberg, pp. 77-92 CrossRef
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• 作者单位：Fundamental Approaches to Software Engineering
• 丛书名：978-3-662-46674-2
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

The progressive industrial adoption of Model-Driven Engineering (MDE) is fostering the development of large tool ecosystems like the Eclipse Modeling project. These tools are built on top of a set of base technologies that have been primarily designed for small-scale scenarios, where models are manually developed. In particular, efficient runtime manipulation for large-scale models is an under-studied problem and this is hampering the application of MDE to several industrial scenarios. In this paper we introduce and evaluate a map-based persistence model for MDE tools. We use this model to build a transparent persistence layer for modeling tools, on top of a map-based database engine. The layer can be plugged into the Eclipse Modeling Framework, lowering execution times and memory consumption levels of other existing approaches. Empirical tests are performed based on a typical industrial scenario, model-driven reverse engineering, where very large software models originate from the analysis of massive code bases. The layer is freely distributed and can be immediately used for enhancing the scalability of any existing Eclipse Modeling tool.