In the era of the Fourth Industrial Revolution, the importance of "Big Data" is increasing enough to be likened to "21st century crude oil." For smart city IoT data, more attention should be paid to quality control because the quality of data leads to...
In the era of the Fourth Industrial Revolution, the importance of "Big Data" is increasing enough to be likened to "21st century crude oil." For smart city IoT data, more attention should be paid to quality control because the quality of data leads to the quality of public services.
Although data quality has been presented by ISO/IEC agencies and by various domestic and foreign agencies through various perspectives, it has a limitation that it is limited to the 'user' center. Data quality indicators centered on 'supplier' are required to collect and deliver data produced by sensors.
To overcome these limitations, this study derived three categories and 13 indicators of supplier-oriented smart city IoT data quality evaluation index based on FGI technique. The data subject to the quality assessment was limited to 'Structured sensor data' that is currently mainly loaded in the data hub of smart city project . Through AHP analysis, the priority of the index categories and data quality indicators were derived, and the one-sample T-test, one-way ANOVA, and reliability analysis were conducted using SPSS 25 to investigate the validity of each indicator based on the four indicator feasibility measurement items. As a result, priorities were derived for each category in the order of "Sensor Data Collection Phase, Data Convergence and Delivery Phase, and Overall Operational Phase" and the final ranking of the indicators was determined in the order of "Confidence, Completeness, Timeliness, Data Volume and Objectivity". In addition, one-sample T-test, one-way ANOVA, and reliability analysis confirmed that the validity of the indicators is guaranteed.
This study is of academic significance in that it derived the smart city IoT sensor-type data quality index from the perspective of the data provider that has not been studied before. In addition, for individuals or entities performing the task of collecting, aggregating and transmitting sensor data, the indicators can contribute to improving sensor data quality by providing the basic requirements that the data should have. Also, data quality control can be carried out based on the index priority derived from the survey of experts in the IoT field to provide an improvement in the efficiency of quality control work.