DQA4AirQuality: TDQM-Based Data Quality Assessment Framework for Air Quality Datasets
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In recent years, open government data and big data analytic applications have become increasingly widespread. Without proper quality control, the rapid dissemination of data may jeopardize the reuse of datasets and exertnegative effects. The current general frameworks for data quality management in literature are outdated andlack extensions to big and open data. In this work, a four-level data quality assessment dimension generationmodel was developed and applied for air quality datasets to measure the quality of air quality data from variousdata quality dimensions. This assessment framework was validated by comparing it with four air qualitydatasets from the World Health Organization (WHO), Beijing, Seoul, and Italy. The results show that thedatasets published by the WHO have low quality due to their more complex sources.
Abstract
In recent years, open government data and big data analytic applications have become increasingly widespread. Without proper quality control, the rapid dissemination of data may jeopardize the reuse of datasets and exertnegative effects. The current general frameworks for data quality management in literature are outdated andlack extensions to big and open data. In this work, a four-level data quality assessment dimension generationmodel was developed and applied for air quality datasets to measure the quality of air quality data from variousdata quality dimensions. This assessment framework was validated by comparing it with four air qualitydatasets from the World Health Organization (WHO), Beijing, Seoul, and Italy. The results show that thedatasets published by the WHO have low quality due to their more complex sources.