A Cross-domain Data Marketplace for Data Sharing

In recent years, vast amounts of data are generated from a plethora of devices, systems, and platforms, covering a wide range of domains. This data increase generates the necessity to access a wide range of technical and technological resources enabling efficient and ready-to-use data analysis solutions, including resources for training and learning in addition to data infrastructure elements, as well as Artificial Intelligence (AI)/Machine Learning (ML) techniques. Current solutions are siloed, and instead of being structured, integrated, and openly accessible from a single-entry point, they still tend to be fragmented and proprietary. To address this gap, the concept of data marketplaces has been generated including a plethora of solutions, in the form of data assets, for offering an access point to the aforementioned services. Though, current data marketplaces lack of genericity since they are tailored and implemented under specific domains, not being able to fully offer a single-entry point towards interdisciplinary ready-to-use data management solutions and assets. This paper describes a cross-domain Data Marketplace, as a unified web-based platform that offers to its users various ready-to-use data management solutions, supporting different kinds of cross-sector assets including datasets, software components, data science notebooks, as well as multimedia content of software solutions among others. Moreover, it showcases how this Data Marketplace has been designed and specified based on existing marketplaces, while it demonstrates the way that its users are able to search and retrieve assets for resolving their business issues, or achieving some of their educational/research/personal goals.

Automated summary

This paper discusses the concept of a cross-domain Data Marketplace as a unified web-based platform that offers various ready-to-use data management solutions to users, supporting different kinds of cross-sector assets including datasets, software components, data science notebooks, and multimedia content.