TQAS 2016 - Workshop on Testing and Quality Assurance and Services for Big Data and Application Systems

With the increase of big data applications in diverse domain, Big Data computing and service is becoming a very hot research and application subject in academic research, industry community, and government services. According to IDC forecasting that the Big Data technology market will "grow at a 27% compound annual growth rate (CAGR) to $32.4 billion through 2017. Today, with the fast advance of big data science, analytics and technology, more and more researchers, businesses, and application professionals are able to use diverse data mining and machine learning algorithms, open-source platforms & tools, as well as cloud database technologies. This suggests that big data computing and application services bring large-scale business requirements and demands in people.s daily life. As more big data and application systems are gradually used in different application domains (such as business, healthcare, transportation, environment monitor and assessment, and smart city development), big data quality and quality assurance of big data based application systems become very important and urgent issues.
The big data scientists and engineers and found that we are lack of research work and results on quality assurance methods & systems, automatic validation solutions, QoS processes, standards and criteria. According to recent reports, quality problems such as disorganized data management, and inconsistent data collection and transform lead to business failures and economic losses. It has been estimated that erroneous data costs US businesses 600 billion dollars annually.
This workshop wants to provide an international platform for academic researchers and practitioners to exchange and discuss the emerging issues, challenges, needs, and solutions on Big Data quality assurance and QoS of big data application systems. Original and research articles are solicited in all aspects including theoretical studies, tools, standards, practical applications, industry experience and experimental prototypes.
All submitted papers will be peer-reviewed and selected on the basis of both their quality and their relevance to the theme of this special issue. Potential topics include, but are not limited to:
Big Data quality assurance standards
Big Data quality control processes and systems
Big Data quality assurance management
Big Data quality validation and automatic tools
Big Data system quality validation methods
QoS for Big data system infrastructures and platforms
Automatic validation methods and tools
QoS quality modeling and study
Big Data QoS parameter validation and evaluation
Knowledge-based for big data quality validation
Quality monitor and visualization
Big data quality services and systems
Big Data application system quality assurance management