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A Method for Directly Deriving a Concise Meta Model from Example Models

Authors:
Bastian Roth
Matthias Jahn
Stefan Jablonski

Keywords: meta model derivation; meta model inference; conciseness of meta models; pattern recognition; language patterns; inheritance

Abstract:
Creating concise meta models manually is a complex task. Hence, newly proposed approaches were developed which follow the idea of inferring meta models from given model examples. They take graphical models as input and primarily analyze graphical properties of the utilized shapes to derive an appropriate meta model. Instead of that, we accept arbitrary model examples independent of a concrete syntax. The contained entity instances may have assigned values to imaginary attributes (i.e., attributes that are not declared yet). Based on these entity instances and the possessed assignments, a meta model is derived in a direct way. However, this meta model is quite bloated with redundant information. To increase its conciseness, we aim to apply so-called language patterns like inheritance and enumerations. For it, the applicability of those patterns is analyzed concerning the available information gathered from the underlying model examples. Furthermore, algorithms are introduced which apply the different patterns to a given meta model.

Pages: 52 to 58

Copyright: Copyright (c) IARIA, 2013

Publication date: May 27, 2013

Published in: conference

ISSN: 2308-3557

ISBN: 978-1-61208-276-9

Location: Valencia, Spain

Dates: from May 27, 2013 to June 1, 2013