{"title":"利用机器学习方法改进事件管理模式","authors":"Roman Jevsejev, Mindaugas Bereiša","doi":"10.3846/mla.2024.21633","DOIUrl":null,"url":null,"abstract":"Technical support of IT infrastructure is a crucial aspect of organizational operations, with the most challenging task being ensuring service continuity. Quality support guarantees high IT efficiency, but complex incidents reduce support quality and require effective management. Incident management includes configuration processes and control of technical solutions. To improve technical support, adhering to both quantitative and qualitative standards and considering system specifics is necessary. According to service level agreements (SLA), the resolution time of incidents is important. „Service Desk“ tools, applying machine learning methods, can help optimize these processes. Incorrectly classified user requests lead to additional work for the IT team and delay incident resolution. Machine learning methods, such as K-means clustering, Random Forest regression, and classification, can optimize incident management and speed up resolution time. The research analyzes „Service Desk“ incident data to model resolution times and improve incident management.","PeriodicalId":509183,"journal":{"name":"Mokslas - Lietuvos ateitis","volume":"71 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"IMPROVEMENT OF INCIDENT MANAGEMENT MODEL USING MACHINE LEARNING METHODS\",\"authors\":\"Roman Jevsejev, Mindaugas Bereiša\",\"doi\":\"10.3846/mla.2024.21633\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Technical support of IT infrastructure is a crucial aspect of organizational operations, with the most challenging task being ensuring service continuity. Quality support guarantees high IT efficiency, but complex incidents reduce support quality and require effective management. Incident management includes configuration processes and control of technical solutions. To improve technical support, adhering to both quantitative and qualitative standards and considering system specifics is necessary. According to service level agreements (SLA), the resolution time of incidents is important. „Service Desk“ tools, applying machine learning methods, can help optimize these processes. Incorrectly classified user requests lead to additional work for the IT team and delay incident resolution. Machine learning methods, such as K-means clustering, Random Forest regression, and classification, can optimize incident management and speed up resolution time. The research analyzes „Service Desk“ incident data to model resolution times and improve incident management.\",\"PeriodicalId\":509183,\"journal\":{\"name\":\"Mokslas - Lietuvos ateitis\",\"volume\":\"71 4\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mokslas - Lietuvos ateitis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3846/mla.2024.21633\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mokslas - Lietuvos ateitis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3846/mla.2024.21633","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
IT 基础设施的技术支持是组织运营的一个重要方面,其中最具挑战性的任务是确保服务的连续性。高质量的支持是 IT 高效率的保证,但复杂的事件会降低支持质量,因此需要有效的管理。事件管理包括技术解决方案的配置流程和控制。要改进技术支持,必须同时遵守定量和定性标准,并考虑系统的具体情况。根据服务水平协议(SLA),事件的解决时间非常重要。应用机器学习方法的 "服务台 "工具可以帮助优化这些流程。对用户请求的不正确分类会给 IT 团队带来额外的工作,并延误事件的解决。K 均值聚类、随机森林回归和分类等机器学习方法可以优化事件管理并加快解决时间。该研究分析了 "服务台 "事件数据,以模拟解决时间并改进事件管理。
IMPROVEMENT OF INCIDENT MANAGEMENT MODEL USING MACHINE LEARNING METHODS
Technical support of IT infrastructure is a crucial aspect of organizational operations, with the most challenging task being ensuring service continuity. Quality support guarantees high IT efficiency, but complex incidents reduce support quality and require effective management. Incident management includes configuration processes and control of technical solutions. To improve technical support, adhering to both quantitative and qualitative standards and considering system specifics is necessary. According to service level agreements (SLA), the resolution time of incidents is important. „Service Desk“ tools, applying machine learning methods, can help optimize these processes. Incorrectly classified user requests lead to additional work for the IT team and delay incident resolution. Machine learning methods, such as K-means clustering, Random Forest regression, and classification, can optimize incident management and speed up resolution time. The research analyzes „Service Desk“ incident data to model resolution times and improve incident management.