In the ACM JDIQ special issue, Andreas Metzger and his co-editors from the Norwegian research organisation SINTEF and Poytechnique Montréal invite submissions on software engineering and AI technologies to solve data quality problems. They are interested in, among other things, new software engineering approaches that use AI models to, for example, preprocess data, detect anomalies in streaming and historical data, repair erroneous data, replace missing data, and detect ethical problems such as bias effects. Similarly, this issue will look at data quality from social, cybersecurity and distribution perspectives. Another topic is the use of public sensor datasets with meta-data on data quality e.g. of CPS/IoT applications (manufacturing, digital health, energy, etc.) to enforce data quality of AI and ML applications.

More information on this special issue:

ACM Journal of Data and Information Quality (JDIQ)

Special Issue on Software Engineering and AI for Data Quality

Guest Editors:

• Sagar Sen, SINTEF (Norway)
• Andreas Metzger, University of Duisburg-Essen (Germany)
• Foutse Khomh, Polytechnique Montréal (Canada)
• Phu Nguyen, SINTEF (Norway)