SEA4DQ 2022

Cyber-physical systems (CPS)/Internet of Things (IoT) are omnipresent in many industrial sectors and application domains in which the quality of the data acquired and used for decision support is a common factor. Data quality can deteriorate due to factors such as sensor faults and failures due to operating in harsh and uncertain environments.

How can software engineering and artificial intelligence (AI) help manage and tame data quality issues in CPS/IoT?

This is the question we aim to investigate in this workshop SEA4DQ. Emerging trends in software engineering need to take data quality management seriously as CPS/IoT are increasingly data-centric in their approach to acquiring and processing data along the edge-fog-cloud continuum. This workshop will provide researchers and practitioners a forum for exchanging ideas, experiences, understanding of the problems, visions for the future, and promising solutions to the problems in data quality in CPS/IoT.

For more details, please visit the workshop webpage. https://sea4dq.github.io

Accepted Papers

Keynotes

Prof. Dr. Andreas Metzger Head of Adaptive Systems and Big Data Applications, University of Duisburg-Essen, Germany

Keynote 1: “Data Quality Issues in Online Reinforcement Learning for Self-adaptive Systems”

A self-adaptive system can modify its structure and behavior at runtime based on its perception of the environment, itself, and its requirements. By adapting itself at runtime, the system can maintain its requirements in the presence of dynamic environment changes. Examples are elastic cloud systems, intelligent IoT systems as well as proactive process management systems. One key element of a self-adaptive system is its self-adaptation logic, which encodes when and how the system should adapt itself. When developing the adaptation logic, developers face the challenge of design time uncertainty. This means they have to anticipate potential environment states and the precise effect of adaptation in a given environment state, while the knowledge available at design time may not be sufficient to do so. A recent industrial survey determined design-time uncertainty as one of the most frequently observed difficulties in designing self-adaptation logic in practice. This talk will explore the opportunities but also challenges that modern machine learning algorithms offer in building the self-adaptation logic in the presence of design-time uncertainty. It will focus on online reinforcement learning as an emerging approach, which means that during operation the system learns from interactions with its environment, thereby effectively leveraging data only available at run time. In particular, the talk will focus on three different issues related to data quality and will introduce initial solutions for these issues: (1) data non-stationarity, (2) data sparsity, and (3) data intransparency. The talk will close with a critical discussion of limitations and an outlook on future research opportunities.

Prof. Foutse Khomh Head of SoftWare Analytics and Technologies (SWAT) Lab, University of Montréal, Canada

Keynote 2: “Data Quality and Model Under-Specification Issues”

Abstract will be added shortly.