Four Generations in Data Engineering for Data Science

Data-driven methods and data science are important scientific methods in many research fields. All data science approaches require professional data engineering components. At the moment, computer science experts are needed for solving these data engineering tasks. Simultaneously, scientists from many fields (like natural sciences, medicine, environmental sciences, and engineering) want to analyse their data autonomously. The arising task for data engineering is the development of tools that can support an automated data curation and are utilisable for domain experts. In this article, we will introduce four generations of data engineering approaches classifying the data engineering technologies of the past and presence. We will show which data engineering tools are needed for the scientific landscape of the next decade.

Application of the global computing curriculum guidelines and skills frameworks for competency discovery and analysis: a case study of data analytics

The current global computing curriculum guidelines including MISI2016, IT2017 and IS2020 are built to promote and facilitate competency-based higher education programs development and to enhance graduate employability. Their applications however are facing challenges in understanding, interpretation and operationalization. Taking data analytics and data engineering, this study shows how these guidelines are used to discover and analyze competencies, the boundaries between typical IT and IS programs and between IS undergraduate and postgraduate programs and further, the gaps for these programs to fill to incorporate professional practice competencies. The global skills frameworks are invoked and SFIA 7 is used to assist analysis.

2021International Conference on Information Technology and Big Data Engineering

2021International Conference on Information Technology and Big Data Engineering were successfully held online from 25th to 27th of April 2021, Wuhan, China. The conference was jointly organized and sponsored by Shaanxi Juxing Exhibition Co., Ltd and Juneng Electronic Technology Co., Ltd. Called by Dr. Wei Wei from Xi'an University of Technology and researcher Jia Han from College of Computer Science, Xi'an Shiyou University, the conference invited scholars and experts in the fields of information technology, big data engineering, computer engineering from various universities to participate in the review and guidance of this conference. The conference focuses on the latest research fields such as "information technology", "big data engineering" and "computer engineering", and aims to provide an international cooperation and exchange platform for experts, scholars and business managers in the fields of information technology, big data engineering, computer engineering application to share their research achievements, discuss the key challenges and research directions of the development of this field, and jointly promote the industrialization cooperation and continuous innovation of international academic achievements. This collection of Proceedings compiles oral and paper presentations submitted by the authors and scrutinized by the Special Committee. List of Organizing Committee, Scientific Committee, Editorial Committee, Invited Speakers, Organizing Institutions, Sponsors are available in this pdf.

Quality Assurance in Big Data Engineering - A Metareview

With a continuously increasing amount and complexity of data being produced and captured, traditional ways of dealing with their storing, processing, analysis and presentation are no longer sufficient, which has led to the emergence of the concept of big data. However, not only the implementation of the corresponding applications is a challenging task, but also the proper quality assurance. To facilitate the latter, in this publication, a comprehensive structured literature metareview on the topic of big data quality assurance is presented. The results will provide interested researchers and practitioners with a solid foundation for their own quality assurance related endeavors and therefore help in advancing the cause of quality assurance in big data as well as the domain of big data in general. Furthermore, based on the findings of the review, worthwhile directions for future research were identified, providing prospective authors with some guidance in this complex environment.