%0 Generic
%A Nikolakopoulos, Anastasios
%A Chondrogiannis, Efthymios
%A Karanastasis, Efstathios
%A Osa, María José López
%A Aroca, Jordi Arjona
%A Kefalogiannis, Michalis
%A Apostolopoulou, Vasiliki
%A Deligeorgi, Efstathia
%A Siopidis, Vasileios
%A Varvarigou, Theodora
%T Scalable Data Profiling for Quality Analytics Extraction
%J IFIP Advances in Information and Communication Technology
%D 2024
%@ 1868-4238
%U https://hdl.handle.net/11556/4844
%X In 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.
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