TY - GEN
T1 - IoT Data Quality
AU - Song, Shaoxu
AU - Zhang, Aoqian
N1 - Publisher Copyright:
© 2020 Owner/Author.
PY - 2020/10/19
Y1 - 2020/10/19
N2 - Data quality issues have been widely recognized in IoT data, and prevent the downstream applications. In this tutorial, we review the state-of-the-art techniques for IoT data quality management. In particular, we discuss how the dedicated approaches improve various data quality dimensions, including validity, completeness and consistency. Among others, we further highlight the recent advances by deep learning techniques for IoT data quality. Finally, we indicate the open problems in IoT data quality management, such as benchmark or interpretation of data quality issues.
AB - Data quality issues have been widely recognized in IoT data, and prevent the downstream applications. In this tutorial, we review the state-of-the-art techniques for IoT data quality management. In particular, we discuss how the dedicated approaches improve various data quality dimensions, including validity, completeness and consistency. Among others, we further highlight the recent advances by deep learning techniques for IoT data quality. Finally, we indicate the open problems in IoT data quality management, such as benchmark or interpretation of data quality issues.
KW - data curation
KW - internet of things
UR - http://www.scopus.com/inward/record.url?scp=85095862698&partnerID=8YFLogxK
U2 - 10.1145/3340531.3412173
DO - 10.1145/3340531.3412173
M3 - Conference contribution
AN - SCOPUS:85095862698
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 3517
EP - 3518
BT - CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 29th ACM International Conference on Information and Knowledge Management, CIKM 2020
Y2 - 19 October 2020 through 23 October 2020
ER -