Automated Data Validation for Data Warehouse Testing

被引:0
作者
Savanur, Sandhya [1 ]
Shreedhara, K. S. [2 ]
机构
[1] UBDT, Davangere, India
[2] UBDT, Dept CSE, Davanagere, India
来源
2016 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, COMMUNICATION, COMPUTER AND OPTIMIZATION TECHNIQUES (ICEECCOT) | 2016年
关键词
Data warehouse; Automation; Data validation; MS Access;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Data warehouse is a system for collecting, organizing, holding and sharing historical data. It is used by the business users for decision support. Business users run queries in one manner or another on the data within the data warehouse environment to support their process. Hence, Data warehouse testing plays an important role in the system. Data warehouse testing comprises of exhaustive testing of a Data warehouse during its design and on an ongoing basis for the incremental activities. Data warehouse testing is common today because of increase in Enterprise Mergers & Acquisitions, Data Center Migrations, Increased Compliance regulations, Senior Management's increased focus on data and data driven decision making. Organization decisions depend entirely on the Enterprise data and the data has to be of utmost quality. Complex Business rules and transformation logic implementations mandates a diligent and thorough testing [3]. The manual data validation and verification process is time consuming and not accurate. This work introduces an automated data validation strategy to reduce the data warehouse testing time thus reducing the testing cost and attaining good data quality in less time.
引用
收藏
页码:223 / 226
页数:4
相关论文
共 50 条
  • [21] Testing Extract-Transform-Load Process in Data Warehouse Systems
    Homayouni, Hajar
    [J]. 2018 29TH IEEE INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING WORKSHOPS (ISSREW), 2018, : 158 - 161
  • [22] Big Data Augmentation with Data Warehouse: A Survey
    Aftab, Umar
    Siddiqui, Ghazanfar Farooq
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2018, : 2785 - 2794
  • [23] A Comparative Analysis of Data Warehouse Data Models
    Bojicic, Ivan
    Marjanovic, Zoran
    Turajlic, Nina
    Petrovic, Marko
    Vuckovic, Milica
    Jovanovic, Vladan
    [J]. 2016 6TH INTERNATIONAL CONFERENCE ON COMPUTERS COMMUNICATIONS AND CONTROL (ICCCC), 2016, : 151 - 159
  • [24] Cacophonic contributions to data quality in the data warehouse
    Rasmussen, Karsten Boye
    [J]. WMSCI 2005: 9TH WORLD MULTI-CONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL 7, 2005, : 311 - 316
  • [25] Extending the data warehouse for service provisioning data
    Kotidis, Yannis
    [J]. DATA & KNOWLEDGE ENGINEERING, 2006, 59 (03) : 700 - 724
  • [26] Data Warehouse Design for Big Data in Academia
    Rudniy, Alex
    [J]. CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 71 (01): : 979 - 992
  • [27] The Applications of Data Mining in Tax Data Warehouse
    Tao, Wang
    Ning, Guo
    [J]. PROCEEDINGS OF THE 2009 INTERNATIONAL CONFERENCE ON SOFTWARE TECHNOLOGY AND ENGINEERING, 2009, : 103 - +
  • [28] Data Integration Patterns for Data Warehouse Automation
    Tomingas, Kalle
    Kliimask, Margus
    Tammet, Tanel
    [J]. NEW TRENDS IN DATABASE AND INFORMATION SYSTEMS II, 2015, 312 : 41 - 55
  • [29] Big Data Augmentation with Data Warehouse: A Survey
    Aftab, Umar
    Siddiqui, Ghazanfar Farooq
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2018, : 2775 - 2784
  • [30] Validation of Data Warehouse Requirements Model Traceability Metrics Using a Formal Framework
    Rakhee
    Kumar, Manoj
    [J]. 2015 2ND INTERNATIONAL CONFERENCE ON COMPUTING FOR SUSTAINABLE GLOBAL DEVELOPMENT (INDIACOM), 2015, : 216 - 221