Browsing by Author "Nikolakopoulos, Anastasios"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Boosting Data Monetisation with DATAMITE(Springer Science and Business Media Deutschland GmbH, 2024) Aroca, Jordi Arjona; Osa, María José López; Barturen, Urtza Iturraspe; Siopidis, Vasileios; Votis, Konstantinos; Nikolakopoulos, Anastasios; Chondrogiannis, Efthymios; Plociennik, Marcin; Himanen, Joel; Marinakis, Achilleas; Nestorakis, Konstantinos; Maglogiannis, Ilias; Iliadis, Lazaros; Karydis, Ioannis; Papaleonidas, Antonios; Chochliouros, Ioannis; HPACompanies around the globe store large quantities of data they cannot monetise. Regarding internal monetisation, they lack tools to facilitate the governance and quality assessment of their data, resulting not really knowing what data they own or it being non reliable due to its poor quality. External monetisation is usually hindered by the unavailability of trustable mechanisms to perform this exchange or enabling the company to participate in ecosystems like EU data spaces or Gaia-X. DATAMITE is an open-source modular and multi-domain framework that focuses on monetisation through interoperability and data exchange. Its modules offer tools for enhancing data governance, quality and security, but also enabling data sharing to a collection of ecosystems like data spaces, Gaia-X, EOSC or AIoD through a plugin-based approach. It also includes a series of additional support tools to assist on data discovery, ingestion, harmonization or evaluate data fairness, among other.Item Scalable Data Profiling for Quality Analytics Extraction(Springer Science and Business Media Deutschland GmbH, 2024) Nikolakopoulos, Anastasios; Chondrogiannis, Efthymios; Karanastasis, Efstathios; Osa, María José López; Aroca, Jordi Arjona; Kefalogiannis, Michalis; Apostolopoulou, Vasiliki; Deligeorgi, Efstathia; Siopidis, Vasileios; Varvarigou, Theodora; Maglogiannis, Ilias; Iliadis, Lazaros; Karydis, Ioannis; Papaleonidas, Antonios; Chochliouros, Ioannis; HPAIn today’s modern society, data play an integral role in the development global industry, since they have become a valuable asset for companies, institutions, governments, and others. At the same time, data generated daily, at a global scale, require significant resources to pre-process, filter and store. When it comes to acquiring such stored data, it is essential to understand which dataset fits to the needs of the user beforehand. One particularly important factor is the quality of a dataset, which could be determined based on a series of quality related attributes generated by it. Such attributes constitute “Profiling”, the process of obtaining information from a data sample, related to the complete dataset’s quality. However, in the era of Big Data, the ability to apply profiling techniques in complete large datasets should also be considered, in order to obtain complete quality insights. This paper attempts to provide a solution for this consideration by presenting “DaQuE”, a scalable framework for efficient profiling and quality analytics extraction in complete datasets of all volumes.