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Scope and aim

In the last decades, a great amount of work has been done in predictive modelling of issues related to human and environment health. Resolution of issues related to healthcare is made possible by the existence of several available biomedical vocabularies and standards, which play a crucial role for understanding health information, together with a large amount of health data. However, focusing solely on healthcare data limits the potential benefits that our lives and societies could have from the rapid development of artificial intelligence (AI) and its enormous capabilities. As such, Lancet Planetary Health in 2019 noted that the focus of future improvements in our wellbeing and societies will depend on investigating the links between food systems, human health, and the environment. However, despite the large number of available resources and work done in the health and environmental domains, there is a lack of resources that can be utilized in the food and nutrition domain, as well as their interconnections. In particular, this is important during the current pandemics of COVID-19, when food provision and security, as well as healthy nutrition and environment, are tremendously needed for quick recovery and long-term sustainable development of our societies.

For the purpose of attaining human and societal wellbeing through advances in the field of artificial intelligence (AI), BFNDMA aims at establishing a global forum for discovering, discussing and communicating AI ideas, methodologies and data-driven solutions that share the common wellbeing goals. The BFNDMA 2021 is a continuation of the successful workshops BFNDMA 2019 organized at IEEE Big Data 2019 in Los Angeles, California, USA and BFNDMA 2020 organized at IEEE Big Data 2020 (virtual event).

Our aim is to primarily focus on methodologies for big data management and analysis of structured and unstructured food, nutrition and environmental data, as well as their synergies and trade-offs. BFNDMA 2021 will consider original and unpublished research articles that propose bold steps towards addressing the challenges of data management and analysis for food, nutrition and environmental data, with strong emphasis on exploring the relation between food systems, human health and environment.




Topics of Interest related to Food and Nutrition

  • Information retrieval, information extraction, natural language processing techniques and artificial intelligence for food,nutrition and environmental sciences;
  • Food and nutrition data normalization;
  • Knowledge representation for food and nutrition;
  • Ontologies, vocabularies and ontology design patterns, with a focus on describing the modelling process for food,nutrition and environmental sciences;
  • Crowdsourcing task designs that have been used and can be (re)used for building resources such as gold standards for food, nutrition environmental data;
  • Predictive and descriptive modeling of food systems, including food production and demand-supply chains;
  • Modeling life on land and underlying ecological and biophysical phenomena from satellite images;
  • COVID-19 impact on food, nutrition, and environment, with regard to their security, diversity, safety and provision.




Invited Speakers


We are working on having sound invited talks. Stay tuned!



Submission

Please submit a full-length paper (up to 10 page IEEE 2-column format), or short or position paper (2-4 page IEEE 2-column format), through the online system.
Papers should be formatted following the IEEE Computer Society Proceedings Manuscript Formatting Guidelines, as instructed by the conference organizers.

Manuscript templates
Microsoft Word
LaTex Formatting Macros

NOTE: In order to participate to this workshop, full or student registration of IEEE BigData 2021 is required for at least one of the authors.




Important dates

  • Paper submission: Oct 1, 2021
  • Decision notification: Nov 1, 2021
  • Camera-ready submission: Nov 15, 2021
  • BFNDMA 2021 Workshop: Dec 15-18, 2021



Organizers

Tome Eftimov, PhD

Computer Systems Department,

Jožef Stefan Institute, Ljubljana, Slovenia

Vladimir Kuzmanovski, PhD

Department of Computer Sciences,

Aalto University, Espoo, Finland

Gjorgjina Cenikj

Computer Systems Department,

Jožef Stefan Institute, Ljubljana, Slovenia

Gordana Ispirova

Computer Systems Department,

Jožef Stefan Institute, Ljubljana, Slovenia

Prof. Barbara Koroušić Seljak, PhD

Computer Systems Department,

Jožef Stefan Institute, Ljubljana, Slovenia

Jaakko Hollmen, PhD

Department of Computer Sciences,

Stockholm University, Stockholm, Sweden




Bibek Paudel, Center for Population Health Sciences, Stanford University, CA, USA
Dragi Kocev, Department of Knowledge Technologies, Jožef Stefan Institute, SI
Duccio Cavalieri, University of Florence, IT
Fabio Mainardi, Nestle Institute of Health Science S.A, Lausanne, CH
Maria Traka, Quadram Institute Bioscience, UK
Petar Ristoski, IBM Research, CA, USA