Models, systems, networks in economics, engineering, nature and society
Background. The need to reasonably determine the priority of processing and resolving a number of streaming requests, user requests, client groups within the framework of complex IT projects, such as service, maintenance of IT infrastructure, including the implementation of streaming iterations, pre...
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| Format: | Article |
| Language: | English |
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Penza State University Publishing House
2024-11-01
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| Series: | Модели, системы, сети в экономике, технике, природе и обществе |
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| Summary: | Background. The need to reasonably determine the priority of processing and resolving a number of streaming requests, user requests, client groups within the framework of complex IT projects, such as service, maintenance of IT infrastructure, including the implementation of streaming iterations, predetermines the need to develop new methods based on the use of aggregated indicators of a number of data. The use of previous methods, including methods of queuing theory, is often irrational or occurs in parallel due to the specifics of the task, the characteristics of iterations, changes in the structure of receipt and processing of service requests, territorial division, automation, informatization and the need to process large streaming data. Materials and methods. A methodological approach to neural network parametric aggregation was used as an aggregation technique. Results. This article proposes an approach to determining management priorities for processing and resolving streaming requests within IT projects based on the aggregation of a number of quality indicators. As part of the work, multi-vector quality characteristics were established that reflect the state of the process of accepting a service request for processing, presenting them in the form of vectors of values with criterion indicators. An aggregated information and analytical indicator of the priority of a single service request was calculated. Conclusions. The indicator of the priority of a single service call, obtained using the neural network parametric aggregation model, correlates with the set of initial data and adequately and generally reflects the totality of initial indicators. In addition, this model makes it possible to predict changes when the qualitative parameters included in the calculation shift. |
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| ISSN: | 2227-8486 |