A Provenance-Aware Data Quality Assessment System

被引:0
|
作者
Zheng, Hua [1 ]
Wu, Kewen [2 ]
Meng, Fei [3 ]
机构
[1] Nanjing Univ, Sch Management & Engn, Nanning 530003, Guang Xi, Peoples R China
[2] Guangxi Univ Finance & Econ, Dept Comp & Informat Management, Nanning 530003, Guang Xi, Peoples R China
[3] Nanjing Univ, Dept Informat & Management, Nanjing 210093, Jiangsu, Peoples R China
来源
COMPUTER SCIENCE FOR ENVIRONMENTAL ENGINEERING AND ECOINFORMATICS, PT 2 | 2011年 / 159卷
关键词
Data quality management; Data quality assessment; Provenance; SOA;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The data quality assessment (DQA) process has the lack of sufficient attention on enterprise infomationization, and existing technologies and methods have their limitations. In order to solve data quality(DQ) problems from the source and realize the traceability of data, after research on data provenance technology and determining the idea of achieving the way data can be traced, the framework of data quality assessment based on data provenance and SOA is presented. Then the logical architecture is described, simultaneously core technology are focus to analyze. Finally, specific application is discussed and the direction of further work is given.
引用
收藏
页码:265 / +
页数:2
相关论文
共 50 条
  • [31] Data Provenance and Security
    McDaniel, Patrick
    IEEE SECURITY & PRIVACY, 2011, 9 (02) : 83 - 85
  • [32] Rule-based data quality assessment and monitoring system in healthcare facilities
    Wang Z.
    Dagtas S.
    Talburt J.
    Baghal A.
    Zozus M.
    Studies in Health Technology and Informatics, 2019, 257 : 460 - 467
  • [33] Cloud-based provenance framework for duplicates identification and data quality enhancement
    Khan, Fakhri Alam
    EXPERT SYSTEMS, 2025, 42 (01)
  • [34] Quality assessment for Linked Data: A Survey
    Zaveri, Amrapali
    Rula, Anisa
    Maurino, Andrea
    Pietrobon, Ricardo
    Lehmann, Jens
    Auer, Soeren
    SEMANTIC WEB, 2016, 7 (01) : 63 - 93
  • [35] On the Importance of Data Quality Assessment of Crowdsourced Meteorological Data
    Vuckovic, Milena
    Schmidt, Johanna
    SUSTAINABILITY, 2023, 15 (08)
  • [36] Data quality assessment: The Hybrid Approach
    Woodall, Philip
    Borek, Alexander
    Parlikad, Ajith Kumar
    INFORMATION & MANAGEMENT, 2013, 50 (07) : 369 - 382
  • [37] Method for Data Quality Assessment of Synthetic Industrial Data
    Iantovics, Laszlo Barna
    Enachescu, Calin
    SENSORS, 2022, 22 (04)
  • [38] Methodologies for Data Quality Assessment and Improvement
    Batini, Carlo
    Cappiello, Cinzia
    Francalanci, Chiara
    Maurino, Andrea
    ACM COMPUTING SURVEYS, 2009, 41 (03)
  • [39] DMN for Data Quality Measurement and Assessment
    Valencia-Parra, Alvaro
    Parody, Luisa
    Jesus Varela-Vaca, Angel
    Caballero, Ismael
    Teresa Gomez-Lopez, Maria
    BUSINESS PROCESS MANAGEMENT WORKSHOPS (BPM 2019), 2019, 362 : 362 - 374
  • [40] An ERP Data Quality Assessment Framework for the Implementation of an APS system using Bayesian Networks
    Herrmann, Jan-Phillip
    Tackenberg, Sven
    Padoano, Elio
    Hartlief, Joerg
    Rautenstengel, Jens
    Loeser, Christine
    Boehme, Joerg
    3RD INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING, 2022, 200 : 194 - 204