ANFIS Modeling to Forecast Maintenance Cost of Associative Information Technology Services
محورهای موضوعی : Data MiningReza Ehtesham Rasi 1 , Leila Moradi 2
1 - Department of Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Department of Information Technology Management, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran
کلید واژه: Information technology , ANFIS modeling , intangible cost , availability , maintenance cost,
چکیده مقاله :
Adaptive Neuro Fuzzy Inference System (ANFIS) was developed for quantifying Information Technology (IT) Generated Services perceptible by business users. In addition to forecasting, IT cost related to system maintenance can help managers for future and constructive decision. This model has been applied by previous large volume of data from IT cost factors, generated services, and associative cost for building pattern, tuning and training this model well. First of all, the model was fully developed, stabilized, and passed through intensive training with large volume of data collected in an organization. It can be possible to feed a specific time period of data into the model to determine the quantity of services and their related maintenance cost. ANFIS forecasting maintenance cost of measured service availability totally provided with first quantifying services in a specific time period. Having an operational mechanism for measuring and quantifying information technology services tangible by users for estimating their costs is contributed to practical accurate investment. Some components have been considered and measured in the field of system maintenance. The main objective of this study was identifying and determining the amount of investment for maintenance of entire generated services by consideration of their relations to tangible cost factors and also intangible cost connected to service lost.
Adaptive Neuro Fuzzy Inference System (ANFIS) was developed for quantifying Information Technology (IT) Generated Services perceptible by business users. In addition to forecasting, IT cost related to system maintenance can help managers for future and constructive decision. This model has been applied by previous large volume of data from IT cost factors, generated services, and associative cost for building pattern, tuning and training this model well. First of all, the model was fully developed, stabilized, and passed through intensive training with large volume of data collected in an organization. It can be possible to feed a specific time period of data into the model to determine the quantity of services and their related maintenance cost. ANFIS forecasting maintenance cost of measured service availability totally provided with first quantifying services in a specific time period. Having an operational mechanism for measuring and quantifying information technology services tangible by users for estimating their costs is contributed to practical accurate investment. Some components have been considered and measured in the field of system maintenance. The main objective of this study was identifying and determining the amount of investment for maintenance of entire generated services by consideration of their relations to tangible cost factors and also intangible cost connected to service lost.
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