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 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

Journal Special Issue

The best experience papers will be recommended to The following Journals: Journal Special Issue Proposal on Quality Assurance and Management for Big Data

To submit your paper to the Special Issue journal, click on the submission link.

Software: Practice and Experience

John Wiley & Sons Ltd

Edited By: Rajkumar Buyya, R. Nigel Horspool
ISI Journal Citation Reports
Ranking: 2014: 57/104 (Computer Science Software Engineering)
ISSN: 1097-024X (electronic version)

International Journal of Data Science and Analytics

Editor-in-Chief: Longbing Cao
ISSN: 2364-415X (print version)
ISSN: 2364-4168 (electronic version)
Journal no. 41060