With the fast advance of smart technologies, computing, and big data technology, many complex cyber application systems are built to improve the quality of people’s life for future smart cities and smart world.

Today, many smart city complex application systems are constructed based on big data and data-driven intelligence. However, according to recent reports, it has been estimated that erroneous data costs US businesses 600 billion dollars annually. Therefore, how to control the quality of big data and data-driven application become a critical practical problem and active research subject.

The proposed workshop aims at providing a platform for both researchers and industry people to exchange the ideas, and discuss the issues and solutions on big data quality assurance and big data application quality validation and automation. The topics of the workshop cover all aspects of big data and application quality assurance, including control processes, standards, models, automatic validation methods, and services. It will meet many emerging needs of both the academic and industry communities.


The workshop invites original papers from both academia and industry. The specific topics of interest include, but are not limited to the following.

A. Big data quality assurance and validation:
• Big data quality modeling and evaluation techniques
• Big data quality validation methods and tools
• Big data quality management and governance
• Big data quality assurance standards and processes
• Big data based quality assurance methods and tools
• Big data cleaning, repair, and quality management
• Quality assurance services and tools for big data

B. Big data application quality assurance:
• Quality assurance standards and models for big data applications
• Validation coverage and analysis for big data applications
• Validation methods and tools for big data applications
• Big data-based application quality assurance and validation
• Test automation for big data based applications
• Quality assurance services and tools for big data applications

C. Big data based quality assurance:
• Big data-based quality validation methods for problem detection, analysis, and prediction
• Data-driven intelligent validation methods and tools for applications
• Big data quality management, economics, and test billing model
• Data-driven test automation in big data applications

D. Open data quality assurance:
• Open data security and privacy
• Open data quality evaluation and assessment
• Open data standards and quality reporting