Empirical rules-based view abstraction for distributed model driven development

Yucong Duan, Jiaxuan Li, Qiang Duan, Lixin Luo, Liang Huang

Research output: Contribution to journalArticle

Abstract

Empirical rules are among the most widely employed approaches for processing view abstraction for UML, which can support model simplification, consistency checking, and complexity reduction. However, empirical rules face challenges such as completeness validation, consistency among rules, and composition priority arrangement. The challenge of composition is enlarged in the environment of distributed model-driven development for web service-based systems, where redundant information/data is emphasised. The same redundant information can be expressed in different forms that comprise different topological structures representing the same part of the system. Such variation will result in choosing different compositions of rules executed in different orders, which will increase the severity of the current non-determinism from the empirical probability of some rules. We investigate the effect of redundancy on rule application through designing a simulated distributed storage for an example model. We construct finite-state automaton to unify empirical rules while relieving the side effects caused by redundancy.

Original languageEnglish (US)
Pages (from-to)192-207
Number of pages16
JournalInternational Journal of Computational Science and Engineering
Volume17
Issue number2
DOIs
StatePublished - 2018

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Modeling and Simulation
  • Computational Theory and Mathematics
  • Hardware and Architecture
  • Software

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