RT Conference Proceedings
T1 Scalable Data Profiling for Quality Analytics Extraction
A1 Nikolakopoulos, Anastasios
A1 Chondrogiannis, Efthymios
A1 Karanastasis, Efstathios
A1 Osa, María José López
A1 Aroca, Jordi Arjona
A1 Kefalogiannis, Michalis
A1 Apostolopoulou, Vasiliki
A1 Deligeorgi, Efstathia
A1 Siopidis, Vasileios
A1 Varvarigou, Theodora
A2 Maglogiannis, Ilias
A2 Iliadis, Lazaros
A2 Karydis, Ioannis
A2 Papaleonidas, Antonios
A2 Chochliouros, Ioannis
AB 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.
PB Springer Science and Business Media Deutschland GmbH
SN 9783031632266
SN 1868-4238
YR 2024
FD 2024
LK https://hdl.handle.net/11556/4844
UL https://hdl.handle.net/11556/4844
LA eng
NO Nikolakopoulos , A , Chondrogiannis , E , Karanastasis , E , Osa , M J L , Aroca , J A , Kefalogiannis , M , Apostolopoulou , V , Deligeorgi , E , Siopidis , V & Varvarigou , T 2024 , Scalable Data Profiling for Quality Analytics Extraction . in I Maglogiannis , L Iliadis , I Karydis , A Papaleonidas & I Chochliouros (eds) , Artificial Intelligence Applications and Innovations. AIAI 2024 IFIP WG 12.5 International Workshops - MHDW 2024, 5G-PINE 2024, and AI4GD 2024, Proceedings . IFIP Advances in Information and Communication Technology , vol. 715 IFIPAICT , Springer Science and Business Media Deutschland GmbH , pp. 177-189 , 13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024 , Corfu , Greece , 27/06/24 . https://doi.org/10.1007/978-3-031-63227-3_12
NO conference
NO Publisher Copyright: © IFIP International Federation for Information Processing 2024.
DS TECNALIA Publications
RD 28 sept 2024