Detection of Composite Operation in Model Management
Author(s) -
Renwei Zhang,
Zheng Qin,
Houbing Song,
Shengnan Li,
Xiao Yang
Publication year - 2017
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2017.2649565
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Model management systems become increasingly critical in model-driven engineering. One of the main tasks of these systems is to record the operations performed on model elements. While most systems support the record of primitive model change operations, complex composite model change operations are neglected, which may result in the lack of understandability. In this paper, we propose an approach to capture model transformation from primitive operations to composite ones. First, based on the low-level operation, we define some general high-level operations with hierarchical structures. Then, a matching algorithm is designed to compare primitive operations with the hierarchical structures from the bottom up. If matching successfully, the primitive operations would be lifted to a composited operation. The algorithm is iterative and ensures that all operations are lifted. The evaluation results on real-world cases show that both precision and recall of composite operation detection are improved when compared with the EMF Modeling Operations (EMO) and Complex Change Detection Engine (CCDE) algorithms.
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