Data-driven decision making powered by Machine Learning (ML) algorithms is changing how the society and the economy work and is having a profound positive impact on our daily life. With the exception of very large companies that have both the data and the skills to develop powerful ML-driven services, the large majority of provably possible ML services, from e-health, to transportation and predictive maintenance, to name just a few, still remain at the idea or prototype level for the simple reason that data, the skills to manipulate them, and the business models to bring them to market, seldom co-exist under the same roof. The value of data comes from its contextualisation and combination with other data. Indeed, this can give way to many new services and products. Furthermore, data has to somehow meet with the ML and business skills that can unleash its full power for the society and economy. This has given rise to a highly dynamic sector around the Data Economy, involving Data Providers/Controllers, data Intermediaries, often-times in the form of Data Marketplaces or Personal Information Management Systems for end-users to control and even monetise their personal data. Despite its huge potential and observed initial growth, the Data Economy is still at its nascent phase and, therefore, faces a yet uncertain future and a series of existential challenges. Such challenges include a broad range of technical matters across multiple disciplines of Computer Science including databases, machine learning, distributed systems, security and privacy, and human computer interaction.

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The mission of the Data Economy workshop will be to bring together all the CS skills required for helping the Data Economy liftoff by addressing a range of technical challenges including, but are not limited, to the ones below:

Design, architecture, systems and protocols for Data Marketplaces, Data Vendors, and Personal Information Management Systems (PIMS)
Consent management and taxonomy of data processing purposes
Sending the data to the algorithm vs. sending the algorithm to the data
Federated and distributed learning in the data economy
Data management and querying in Data Marketplaces
Secure data exchange and delivery mechanisms
Federated data catalogues and data discovery mechanisms
Heterogeneous and federated DBMS, metadata management
Information Integration and Data Quality
Privacy/data protection and the data economy
Data pricing mechanisms for individual and aggregated data
How to buy data – data purchase policies and algorithms
Protecting data ownership rights for commercial datasets
NFTs, blockchains, smart contracts and their role in the data economy
Trusted execution, cloud computing, distributed storage and their role in the data economy
Data representation and exchange standards in the data economy
Understanding the value of data in different applications and domains and across the data value chain
Cryptographic approaches, including FHE, SMPC, DP, and their role and limits in the data economy
UX and HCI challenges for data marketplaces and PIMS
Understanding and decoding the Terms & Conditions of data marketplaces and data transactions
Federation and interoperability standards and protocols in the data economy
Measurement studies related to the data economy
Large-scale experiments and validation studies for the data economy