| Title | Unsupervised Anomaly Detection in Data Quality Control |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Poon, Lex, Farshidi, Siamak, Li, Na, Zhao, Zhiming |
| Conference Name | 2021 IEEE International Conference on Big Data (Big Data) |
| Date Published | dec |
| Keywords | anomaly detection, automated data quality control, Big Data, data integrity, Data models, data quality assessment, data quality control, decision making, human in the loop, Organizations, process control, pubcrawl, Quality Control, Scalability, unsupervised learning |
| Abstract | Data is one of the most valuable assets of an organization and has a tremendous impact on its long-term success and decision-making processes. Typically, organizational data error and outlier detection processes perform manually and reactively, making them time-consuming and prone to human errors. Additionally, rich data types, unlabeled data, and increased volume have made such data more complex. Accordingly, an automated anomaly detection approach is required to improve data management and quality control processes. This study introduces an unsupervised anomaly detection approach based on models comparison, consensus learning, and a combination of rules of thumb with iterative hyper-parameter tuning to increase data quality. Furthermore, a domain expert is considered a human in the loop to evaluate and check the data quality and to judge the output of the unsupervised model. An experiment has been conducted to assess the proposed approach in the context of a case study. The experiment results confirm that the proposed approach can improve the quality of organizational data and facilitate anomaly detection processes. |
| DOI | 10.1109/BigData52589.2021.9671672 |
| Citation Key | poon_unsupervised_2021 |
Unsupervised Anomaly Detection in Data Quality Control