A Novel Framework for Integrating Heterogeneous Data Sources through Data Exchange

被引:0
|
作者
Cheng, Yin -Ting [1 ]
Chen, Ming-Chih [1 ]
机构
[1] Natl Kaohsiung Univ Sci & Technol, Dept Elect Engn, First Campus,1,Univ Rd, Kaohsiung 82445, Taiwan
关键词
data integration; modularized agent; interface; artificial intelligence;
D O I
10.18494/SAM4299
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
We present a design framework for an electronic system that facilitates the integration of heterogeneous data sources. To meet the specific requirements of data manipulation, different database systems need to exchange their data content to fulfill the needs of their respective systems. To address this challenge, a data exchange mechanism is proposed to enable the integration of external data from multiple sources and facilitate data exchange among multiple systems. Within this framework, a data exchange agent (DE-Agent) is designed to handle the integration and distribution of multiple external data sources into the internal system. The DEAgent acts as an intermediary, effectively transmitting external data to the system's database. Experimental results demonstrate that the designed DE-Agent is capable of supporting stimulus operations from over forty inspectors while maintaining the correct and efficient functioning of the system. This research contributes to the development of an electronic system that effectively integrates heterogeneous data sources. The proposed design framework and DE-Agent provide a reliable mechanism for data exchange, enabling seamless integration between different database systems and ensuring the proper functioning of the overall system.
引用
收藏
页码:2603 / 2618
页数:16
相关论文
共 50 条
  • [31] Working framework of semantic interoperability for CRIS with heterogeneous data sources
    Leiva-Mederos, Amed
    Senso, Jose A.
    Hidalgo-Delgado, Yusniel
    Hipola, Pedro
    [J]. JOURNAL OF DOCUMENTATION, 2017, 73 (03) : 481 - 499
  • [32] Lynx: A Graph Query Framework for Multiple Heterogeneous Data Sources
    Shen, Zhihong
    Hu, Chuan
    Zhao, Zihao
    [J]. PROCEEDINGS OF THE VLDB ENDOWMENT, 2023, 16 (12): : 3926 - 3929
  • [33] A framework for abstracting data sources having heterogeneous representation formats
    Rosaci, D
    Terracina, G
    Ursino, D
    [J]. DATA & KNOWLEDGE ENGINEERING, 2004, 48 (01) : 1 - 38
  • [34] Integrating Heterogeneous Data for a Multi-disease Outbreak Detection Framework
    Villanueva-Miranda, Ismael
    Akbar, Monika
    [J]. 2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2021, : 2828 - 2837
  • [35] A Novel XML-based Power Resource Modeling Framework for Power System Heterogeneous Data Integrating
    Wan, Can
    Dong, Zhaoyang
    Luo, Fengji
    Wang, Rui
    Chen, Yingying
    Meng, Ke
    Wong, Kit-Po
    [J]. INFORMATION TECHNOLOGY FOR MANUFACTURING SYSTEMS II, PTS 1-3, 2011, 58-60 : 1476 - +
  • [36] A Novel Semantic Approach for Integrating Heterogeneous Data for Tourism Information
    Monisha, G. S.
    Srinivasulu, Senduru
    Paul, J. Sapphire
    [J]. RESEARCH JOURNAL OF PHARMACEUTICAL BIOLOGICAL AND CHEMICAL SCIENCES, 2016, 7 (04): : 264 - 273
  • [37] Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources
    Zhou, Zhongbao
    Gao, Meng
    Xiao, Helu
    Wang, Rui
    Liu, Wenbin
    [J]. OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, 2021, 104
  • [38] Integrating Heterogeneous Data Sources for Cross-Institutional Data Sharing: Requirements Elicitation and Management in SMITH
    Tahar, Kais
    Mueller, Christoph
    Duerschmid, Andreas
    Haferkamp, Silke
    Saleh, Kutaiba
    Juers, Patrick
    Staeubert, Sebastian
    Gewehr, Jan Erik
    Zenker, Sven
    Ammon, Danny
    Wendt, Thomas
    [J]. MEDINFO 2019: HEALTH AND WELLBEING E-NETWORKS FOR ALL, 2019, 264 : 1785 - 1786
  • [39] A framework of integrating heterogeneous data sources for monthly streamflow prediction using a state-of-the-art deep learning model
    Xu, Wenxin
    Chen, Jie
    Zhang, Xunchang J.
    Xiong, Lihua
    Chen, Hua
    [J]. JOURNAL OF HYDROLOGY, 2022, 614
  • [40] A framework of integrating heterogeneous data sources for monthly streamflow prediction using a state-of-the-art deep learning model
    Xu, Wenxin
    Chen, Jie
    Zhang, Xunchang J.
    Xiong, Lihua
    Chen, Hua
    [J]. Journal of Hydrology, 2022, 614