Conference paper Open Access

Unsupervised Anomaly Detection in Data Quality Control

Poon, Lex; Farshidi, Siamak; Li, Na; Zhao, Zhiming


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    <dcat:keyword>data quality</dcat:keyword>
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    <dcat:keyword>data quality control</dcat:keyword>
    <dcat:keyword>data quality assessment</dcat:keyword>
    <dcat:keyword>anomaly detection,</dcat:keyword>
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    <dct:description>&lt;p&gt;Data is one of the most valuable assets of an&lt;/p&gt; &lt;p&gt;organization and has a tremendous impact on its long-term&lt;/p&gt; &lt;p&gt;success and decision-making processes. Typically, organizational&lt;/p&gt; &lt;p&gt;data error and outlier detection processes perform manually and&lt;/p&gt; &lt;p&gt;reactively, making them time-consuming and prone to human errors.&lt;/p&gt; &lt;p&gt;Additionally, rich data types, unlabeled data, and increased&lt;/p&gt; &lt;p&gt;volume have made such data more complex. Accordingly, an&lt;/p&gt; &lt;p&gt;automated anomaly detection approach is required to improve&lt;/p&gt; &lt;p&gt;data management and quality control processes. This study&lt;/p&gt; &lt;p&gt;introduces an unsupervised anomaly detection approach based&lt;/p&gt; &lt;p&gt;on models comparison, consensus learning, and a combination of&lt;/p&gt; &lt;p&gt;rules of thumb with iterative hyper-parameter tuning to increase&lt;/p&gt; &lt;p&gt;data quality. Furthermore, a domain expert is considered a&lt;/p&gt; &lt;p&gt;human in the loop to evaluate and check the data quality and to&lt;/p&gt; &lt;p&gt;judge the output of the unsupervised model. An experiment has&lt;/p&gt; &lt;p&gt;been conducted to assess the proposed approach in the context of&lt;/p&gt; &lt;p&gt;a case study. The experiment results confirm that the proposed&lt;/p&gt; &lt;p&gt;approach can improve the quality of&lt;/p&gt;</dct:description>
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