Intrinsic and extrinsic quality of data for open data repositories

被引：2

作者：

Gonzalez-Vidal, Aurora ^{[1
]}

Ramallo-Gonzalez, Alfonso P. ^{[1
]}

Skarmeta, Antonio F. ^{[1
]}

机构：

[1] Univ Murcia, Dept Informat & Commun Engn, Murcia, Spain

来源：

ICT EXPRESS | 2022年 / 8卷 / 03期

关键词：

Data quality; Open data; IoT; Machine learning;

D O I：

10.1016/j.icte.2022.06.001

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This work assesses the quality of Internet of Things data not only as an intrinsic quality on how well it represents the related phenomenon but also, on how much information it contains to educate an artificial entity. The quality metrics here proposed are tested with real datasets. Also, they are implemented on OpenCPU, so the open data repositories can use them off-the-shelf to rate their datasets without computational cost and minimum human intervention, making them more attractive to potential users and gaining visibility and impact. (C) 2022 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences.

引用

页码：328 / 333

页数：6

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