QUALITY ANALYSIS OF A CROSS-DOMAIN REFERENCE ARCHITECTURE

被引:0
|
作者
Dobrica, Liliana [1 ]
Ovaska, Eila [2 ]
机构
[1] Univ Politehn Bucuresti, Fac Automat Control & Comp, Spl Independentei 313, Bucharest, Romania
[2] VTT Tech Res Ctr Finland, Oulu, Finland
关键词
Cross Domain Reference Architecture; Service; Quality; Analysis Methods; Scenarios;
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
The content of this paper addresses the issue of how to perform in a systematic way quality analysis of a cross domain reference architecture using scenarios. The cross domain reference architecture is designed based on the domains requirements and features modelling and it includes domains core services and constraints on how these services should be combined. We apply a method based on scenarios to analyse modifiability at the architectural level. In order to handle complexity in analysis we propose categories of change scenarios to be derived from each problem domain. Our main concerns are core services changes in the scenarios interaction step.
引用
收藏
页码:157 / +
页数:2
相关论文
共 50 条
  • [41] Cross-Domain Feature Similarity Guided Blind Image Quality Assessment
    Feng, Chenxi
    Ye, Long
    Zhang, Qin
    FRONTIERS IN NEUROSCIENCE, 2022, 15
  • [42] Towards the Representation of Cross-Domain Quality Knowledge for Efficient Data Analytics
    Kropatschek, Sebastian
    Steuer, Thorsten
    Kiesling, Elmar
    Meixner, Kristof
    Fruehwirth, Thomas
    Sommer, Patrik
    Schachinger, Daniel
    Biffl, Stefan
    2021 26TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), 2021,
  • [43] Domain transfer via cross-domain analogy
    Klenk, Matthew
    Forbus, Ken
    COGNITIVE SYSTEMS RESEARCH, 2009, 10 (03) : 240 - 250
  • [44] Translation as Cross-Domain Knowledge: Attention Augmentation for Unsupervised Cross-Domain Segmenting and Labeling Tasks
    Luo, Ruixuan
    Zhang, Yi
    Chen, Sishuo
    Sun, Xu
    FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, EMNLP 2021, 2021, : 1896 - 1906
  • [45] Cross-Domain Gated Learning for Domain Generalization
    Dapeng Du
    Jiawei Chen
    Yuexiang Li
    Kai Ma
    Gangshan Wu
    Yefeng Zheng
    Limin Wang
    International Journal of Computer Vision, 2022, 130 : 2842 - 2857
  • [46] Cross-domain Ensemble Distillation for Domain Generalization
    Lee, Kyungmoon
    Kim, Sungyeon
    Kwak, Suha
    COMPUTER VISION, ECCV 2022, PT XXV, 2022, 13685 : 1 - 20
  • [47] Cross-Domain Gated Learning for Domain Generalization
    Du, Dapeng
    Chen, Jiawei
    Li, Yuexiang
    Ma, Kai
    Wu, Gangshan
    Zheng, Yefeng
    Wang, Limin
    INTERNATIONAL JOURNAL OF COMPUTER VISION, 2022, 130 (11) : 2842 - 2857
  • [48] Exploring the Cross-Domain Action Recognition Problem by Deep Feature Learning and Cross-Domain Learning
    Gao, Zan
    Han, T. T.
    Zhu, Lei
    Zhang, Hua
    Wang, Yinglong
    IEEE ACCESS, 2018, 6 : 68989 - 69008
  • [49] Cross-Domain Feature Augmentation for Domain Generalization
    Liu, Yingnan
    Zou, Yingtian
    Qiao, Rui
    Liu, Fusheng
    Lee, Mong Li
    Hsu, Wynne
    PROCEEDINGS OF THE THIRTY-THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2024, 2024, : 1146 - 1154
  • [50] The Role of Cross-Domain Use Cases in IoT - A Case Analysis
    Baer, Sebastian
    Reinhold, Olaf
    Alt, Rainer
    PROCEEDINGS OF THE 52ND ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, 2019, : 390 - 399