About SEA4DQ

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.

Topics of Interest

  • Software/hardware architectures and frameworks for data quality management in CPS/IoT
  • Software engineering and AI to pre-process and clean data
  • Software engineering and AI to detect and repair anomalies in CPS/IoT data
  • Software engineering and AI to cluster data as events
  • Software tools for data quality management, testing, and profiling
  • Public sensor datasets from CPS/IoT (manufacturing, digital health, energy,...)
  • Distributed ledger and blockchain technologies for quality tracking
  • Quantification of data quality hallmarks and uncertainty in data repair
  • Sensor data fusion techniques for improving data quality and prediction
  • Augmented data quality
  • Case studies that have evaluated an existing technique or tool on real systems, not only toy problems, to manage data quality in cyber-physical systems in different sectors.
  • Certification and standardization of data quality in CPS/IoT
  • Approaches for secure and trusted data sharing, especially for data quality, management, and governance in CPS/IoT
  • Trade-offs between data quality and data security in CPS/IoT

Call for Papers

SEA4DQ 2022 accepts the following types of contributions:

  • Position Papers (max. 2 pages) that analyze trends in data quality for CPS/IoT and raise issues of importance. Position papers are intended to generate discussion and debate during the workshop, and will be reviewed with respect to relevance and their ability to start up fruitful discussions;
  • Work-in-progress Papers (max. 4 pages) that describe novel, interesting, and highly potential work in progress, but not necessarily reaching its full completion;
  • Full Papers (max. 10 pages) describing original and completed research -- either empirical or theoretical -- in techniques, tools, or industrial case studies;
  • Tool Papers/Demos/Posters (max. 4 pages) presenting some tools/demos/posters that are related to data quality;
  • Presentation Abstracts that that will be around 250-500 words long (and presentation files if possible). If accepted, authors will give presentations at the workshop about (i) research results that are either already published or early research results not yet published; and (ii) industrial talks. This new track aims at stimulating the participation of industrial practitioners - who will be able to present the practices used in their contexts - as well as researchers - who may be interested in receiving feedback from the research community on early ideas. The abstract will only be reviewed by the program committee for relevance, and will not be included in the workshop proceedings.

All submissions must be in English and in PDF format. Submission Format: ACM Primary Article Template.

The accepted papers will be published in the workshop's proceedings (will be proposed for publication in the ACM digital library). As a published ACM author, you and your co-authors are subject to all ACM Publications Policies, including ACM’s new Publications Policy on Research Involving Human Participants and Subjects. At least one author of each accepted paper must register and present the paper in person at SEA4DQ 2022 in order for the paper to be published in the proceedings.

Important Dates

  • Abstract submission deadline: July 25, 2022 (optional)
  • Submission deadline: August 1, 2022
  • Notification of Acceptance: August 26, 2022
  • Camera-Ready Submission: September 9, 2022 (hard deadline)
  • Workshop: November 17, 2022



Prof. Dr. Andreas Metzger

Head of Adaptive Systems and Big Data Applications,
University of Duisburg-Essen, Germany

Title: "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 that encodes when and how the system should adapt itself. When developing the adaptation logic, developers face the challenge of design time uncertainty. To define when the system should adapt, they have to anticipate all potential environment states. However, this is infeasible in most cases due to incomplete information at design time. As an example, the concrete services that may be dynamically bound during the execution of a service-oriented system and thus their quality characteristics are typically not known at design time. To define how the system should adapt itself, developers need to know the precise effect an adaptation action has. However, the precise effect may not be known at design time. As an example, while developers may know in principle that enabling more features will negatively influence the performance, exactly determining the performance impact is more challenging. A recent industrial survey determined optimal design and design complexity together with design-time uncertainty to be among 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 introduce our solutions for (1) coping with large discrete adaptation spaces, and (2) handling non-stationary environments. The talk will close with a critical discussion of limitations and an outlook on future research opportunities.


The schedule is currently being defined and will be announced shortly

Registration can be done at the FSE conference website.

Organization Committee

Phu Nguyen (Main Contact)
General Chair
SINTEF, Norway
Sagar Sen (Main Contact)
Co-Program Chair
SINTEF, Norway
Maria Chiara Magnanini
Co-Program Chair
Politecnico di Milano, Italy

Beatriz Cassoli
Moderator, Co-Web Chair
TU Darmstadt, Germany
Nicolas Jourdan
Moderator, Co-Web Chair
TU Darmstadt, Germany
Mikel Armendia
Publicity Chair
Tekniker, Spain

Program Committee (TBC)*

  • Andreas Metzger, University of Duisburg-Essen, Germany
  • Jean-Yves Tigli, Université Côte d’Azur, France
  • Helena Holmström Olsson, Malmö University, Sweden
  • Frank Alexander Kraemer, NTNU, Norway
  • Hong-Linh Truong, Aalto University, Finland
  • Cyril Cecchinel, DataThings, Luxembourg
  • Dumitru Roman, SINTEF / University of Oslo, Norway
  • Felix Mannhardt, KIT-AR, Germany
  • Dimitra Politaki, INLECOM, Greece
  • Amina Ziegenbein, Technische Universität Darmstadt, Germany
  • Flavien Peysson, PREDICT, France
  • Karl John Pedersen, DNV AS, Norway
  • Abhilash Anand, DNV AS, Norway
  • Helge Spieker, Simula Research Laboratory, Norway
  • Dusica Marijan, Simula Research Laboratory, Norway
  • Marc Roper, University of Strathclyde, UK
  • Jan Nygård, Cancer Registry of Norway, Norway
  • Freddy Munoz, Compass Inc., USA
  • Stefano Borgia, Holonix, Italy
  • Katinka Wolter, Free University of Berlin, Germany
  • Sudipto Ghosh, Colorado State University, USA
  • Luke Todhunter, University of Nottingham, UK
  • Debmalya Biswas, Darwin Digital, Switzerland
  • Enrique Garcia Ceja, Optimeering, Oslo
* PC members list is in an arbitrary order.

The SEA4DQ 2022 Workshop is sponsored by the research projects InterQ and DAT4.Zero that are funded by the European Union’s Horizon 2020 Research and Innovation programme.