Don’t miss an action-packed program with sessions that will go from the most recent innovations around Big Data to technical and business use cases and workshops. The event will be focusing on increasing collaboration and opening new opportunities for all actors and stakeholders part of this ecosystem. Lessons and shared experiences will serve to promote the use of Big Data across sectors, extracting value from smart data management and Artificial Intelligence informed decisions.

Day 14th May

15:00 | Data Sharing, DataSet and associated Business Model
This session will be focused on sharing experiences among projects about the needs for data sharing inside and outside them, as well as how the data management plans being developed in each of them is being handled.

17:00 | Technology solutions for privacy issues: what is the best way forward?
Can technology guarantee the anonymization of personal data without losing the value added of analytics? What is the best approach to designing privacy-aware solutions and services without falling into ethical traps, such as fostering discrimination and unfairness? Which data technologies are best to preserve privacy and security? Can we move from technology as the problem (violating privacy) to technology as the solution? This workshop will start from the project e-SIDES analysis and classification of the main PETs (privacy-enhancing technologies) to assess with participants the state of the art, discuss the emerging challenges, and carry out a collaborative discussion on the possible guidelines of responsible research and innovation in this field. A fully interactive and open discussion workshop.

17:00 | Towards a Federation of European Data Spaces
The objective of this session is to identify the areas of collaboration between the initiatives that have innovation based on data analytics at the core of their initiatives. We will discuss the different steps that need to be taken for a Federation of Data Spaces, starting from the most collaborative ones (Identifying and mapping expertise, service provision models, training programmes…) to evolve to the most complex ones (federated data access, federated resource usage, privacy and security in federated access…).

17:00 | Software approaches for managing Big Data
During this session, we will discuss possible different approaches for managing Big Data: from the perspective of a large company and how it uses Big Data and Machine Learning to manage its landscape, to a model-based Big Data Analytics as a service approach, and how to unlock IoT streams with adding context, presenting a specific case in agriculture.


Day 15th May

9:30 | Transport in Urban Scenarios
This session will address specific technologies and innovations that transport-related projects deliver and demonstrate in their pilots/use cases, which could be of interest for other projects to share and find common paths to collaborate. In addition, transport projects will present project assets that could be used by other projects in relation to areas touched by the BDVA Mobility and Logistics Subgroup.

9:30 | From Big Data to Big Challenges in Manufacturing domain
The implementation of data-driven approaches in the manufacturing domain is not always easy and straightforward as in our dreams. Different various challenges obstacle the path to the success and they need to be carefully identified in order to implement the most proper and effective solution. The session will have two main focus. The first on non-technical priorities as a new topic to include in the second version of the Discussion Paper published by the Smart Manufacturing Industry group of BDVA, that will be briefly presented. The second on Reference Architectures and challenges for big data-driven industrial digital transformation implementation, thanks to the involvement of BOOST 4.0, the lighthouse project on manufacturing that just started.

11:30 | Success Patterns of Data-driven Business OpportunitiesHow to identify promising data-driven business opportunities?
Are there best practices or success patterns one can rely on? These are the two questions that are central to this session. Based on our results of a quantitative and representative study of data-driven business opportunities, we will introduce patterns and success criteria of successful data-driven start-ups. Along with concrete examples and success stories, we will illustrate their ideas, offerings and value network strategy. At the end of the session, we will explore implications of these findings for your own project and for potential business ideas of project members. Expected Outcome: participants know how to identify and scope promising data-driven business opportunities as guidance for their future investment decisions.

11:30 | FATE of AI
The workshop will provide a reflection on different approaches on how to make fairness, accountability, transparency, and ethics (FATE) tangible for both ‘teachers’ (developers/data curators) and ‘learners’ (models) in the AI classroom. FATE is as essential a resource in this classroom as data. Yet, we still don’t quite know whether teaching machine ethics or teaching ethics to machines is the way to go. We will discuss and approach different components such as transparency, interpretability, explainability, validation, verification and standardisation from the perspective and in collaboration with selected PPP projects. By jointly presenting their assessments, we will be able to cross-fertilize across the projects and to identify approaches and open issues to be further researched in the AI/Big Data community. As such, the session will contribute to the SRIA of the BDVA and has relevance for the societal acceptance of AI/Big Data solutions. We explore why the moral and legal compass for AI does not live in books and how it could find its place in the AI classroom, where machine learning becomes symmathesy or learning together.

16:00 | Leveraging the human factor in the BDV Chain
Putting humans in the loop of Big Data Value chains can be the perfect complement for purely automated approaches: Collecting data when sensors are missing, label data for training machine learning algorithms or as a necessary step when personal data is being used. In this session, we will discuss how different projects are leveraging the human factor for the Big Data Value Chain. What is the value of human computation for different data value chain? How are the engagement strategies (for users) or expected costs (crowdsourcing?) What assets have been developed (or plan to develop) The goals are: 1) To exchange experiences and find possible synergies among the different projects that are currently putting humans in the loop, and 2) Explore the potential use of these techniques in other projects that did not consider it initially but for which it might be useful.