About SEA4DQ

Cyber-physical systems (CPS) have been developed 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?

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 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.

Topics of Interest

  • Software/hardware co-design and architectures and frameworks for data quality management in CPS
  • Software engineering and AI to detect anomalies in CPS data
  • Software engineering and AI to repair erroneous CPS data
  • Software tools for data quality management, testing, and profiling
  • Public sensor datasets from CPS (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
  • Approaches for secure and trusted data sharing, especially for data quality, management, and governance in CPS
  • Trade-offs between data quality and data security in CPS


Dr. Andreas Metzger

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

Keynote title to be announced.

Dr. Andreas Metzger is senior academic councilor at the University of Duisburg-Essen and heads the Adaptive Systems and Big Data Applications group at paluno, the Ruhr Institute for Software Technology. He holds a PhD in computer science from the Technical University of Kaiserslautern. His background and research interests are software engineering and machine learning for self-adaptive systems. He has co-authored over 120 papers, articles and book chapters. His recent research on online reinforcement learning for self-adaptive software systems received the Best Paper Award at the Int’l Conference on Service-oriented Computing. He is co-organiser of over 15 international workshops and conference tracks, and program committee member for numerous international conferences. Andreas was Technical Coordinator of the European lighthouse project TransformingTransport, which demonstrated in a realistic, measurable, and replicable way the transformations that big data and machine learning can bring to the mobility and logistics sector. In addition, he was member of the Big Data Expert Group of PICASSO, an EU-US collaboration action on ICT topics. Andreas serves as steering committee vice chair of NESSI (the European Technology Platform dedicated to Software, Services and Data), and as deputy secretary general of BDVA/DAIRO (the Big Data Value Association).

Important Dates

  • Abstract Registration: May 25. 2021
  • Paper Submission: May 1. - June 4. 2021
  • Notification of Acceptance: July 1. 2021
  • Camera-Ready Submission: July 8. 2021
  • Workshop: August 23. 2021

Organization Committee


Phu Nguyen (Main Contact)

Publicity Chair
SINTEF, Norway

Sagar Sen (Main Contact)

Co-Program Chair
SINTEF, Norway

Mikel Armendia
Co-General Chair
Tekniker, Spain
Odd Myklebust
Co-General Chair
SINTEF, Norway
Per Myrseth
Co-Program Chair
DNV, Norway
Beatriz Cassoli
Co-Web Chair
TU Darmstadt, Germany
Nicolas Jourdan
Co-Web Chair
TU Darmstadt, Germany

Program Committee (to be confirmed)*

  • Andreas Metzger, University of Duisburg-Essen, Germany
  • Mike Papadakis, University of Luxembourg, Luxembourg
  • Donghwan Shin, University of Luxembourg, Luxembourg
  • David Lo, Singapore Management University, Singapore
  • Jean-Yves Tigli, Université Côte d’Azur, France
  • Frank Alexander Kraemer, NTNU, Norway
  • Hong-Linh Truong, Aalto University, Finland
  • Francois Fouquet, DataThings, Luxembourg
  • Dumitru Roman, SINTEF / University of Oslo, Norway
  • Enrique Garcia-Ceja, SINTEF, 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
  • Xavier Beaudaert, IDEKO, Spain
  • 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
  • Nelly Bencomo, Aston University, UK
  • Hugo Bruneliere, IMT-Atlantique, France
  • Katinka Wolter, Free University of Berlin, Germany
  • Sudipto Ghosh, Colorado State University, USA
  • Luke Todhunter, University of Nottingham, UK
  • Nils Henrik Holmedahl, LHL, Norway
  • Debmalya Biswas, Darwin Digital, Switzerland
  • Eduardo Cunha de Almeida, Universidade Federal do Paraná, Brazil
* PC members list is in an arbitrary order.

Call for Papers

SEA4DQ 2021 accepts the following types of contributions:

  • Position Papers (max. 2 pages) that analyze trends in data quality for CPS 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.

Papers and abstracts must follow the ESEC/FSE 2021 formatting instructions. Please submit the papers in PDF format on EasyChair. Papers and talk proposals should be original and unpublished material describing innovative and mature research results, experience reports, case studies, challenges, problems and solutions, ongoing work, new ideas, new results and future trends.

All submissions will be reviewed by three program committee members. The program committee will review all submissions for relevance, potential to trigger discussions at the workshop, lessons learned, quality of presentation, and novelty.

The accepted workshop papers, both regular and short, and two page extended abstracts will be published in the ESEC/FSE 2021 workshop proceedings in the ACM Digital Library. Authors of accepted papers and talks are required to register and present the paper at the workshop for the paper and or extended abstract to be included in the proceedings. The official publication date of the workshop proceedings is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks prior to the first day of ESEC/FSE 2021. The official publication date affects the deadline for any patent filings related to published work.


The SEA4DQ 2021 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.