• Journal of Service Research and Studies
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• v.11 no.1
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• pp.45-58
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• 2021

Requirements engineering can be seen as proceeding with various processes and activities such as extraction, analysis, specification, management, and verification without temporal and spatial constraints in the development environment of information systems that are becoming large and decentralized. Developing requirements well and conducting continuous evaluation and management is the shortcut to success in project management, and it is recognized as a very important matter in relation to requirements in the information system audit. When we conduct information system audit and conducting projects subject to audit, we need to improve the required engineering aspect. Therefore, this study derives inspection items suitable for the target project by referring to the audit inspection manual and audit inspection guide when conducting the current audit, and relates to the required engineering aspect among the contents of the inspection guide for each business type that is the basis for deriving the inspection items were derived for each audit point/audit area for the project management and quality assurance project type corresponding to the inspection items. The suitability of the extracted occupation items was verified through a questionnaire survey by experts.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.