没关系,问题已经解决了:主动检测和排除客户DSL问题

Yu Jin, N. Duffield, Alexandre Gerber, P. Haffner, S. Sen, Zhi-Li Zhang
{"title":"没关系,问题已经解决了:主动检测和排除客户DSL问题","authors":"Yu Jin, N. Duffield, Alexandre Gerber, P. Haffner, S. Sen, Zhi-Li Zhang","doi":"10.1145/1921168.1921178","DOIUrl":null,"url":null,"abstract":"Traditional DSL troubleshooting solutions are reactive, relying mainly on customers to report problems, and tend to be labor-intensive, time consuming, prone to incorrect resolutions and overall can contribute to increased customer dissatisfaction. In this paper, we propose a proactive approach to facilitate troubleshooting customer edge problems and reducing customer tickets. Our system consists of: i) a ticket predictor which predicts future customer tickets; and ii) a trouble locator which helps technicians accelerate the troubleshooting process during field dispatches. Both components infer future tickets and trouble locations based on existing sparse line measurements, and the inference models are constructed automatically using supervised machine learning techniques. We propose several novel techniques to address the operational constraints in DSL networks and to enhance the accuracy of NEVERMIND. Extensive evaluations using an entire year worth of customer tickets and measurement data from a large network show that our method can predict thousands of future customer tickets per week with high accuracy and signifcantly reduce the time and effort for diagnosing these tickets. This is benefcial as it has the effect of both reducing the number of customer care calls and improving customer satisfaction.","PeriodicalId":20688,"journal":{"name":"Proceedings of The 6th International Conference on Innovation in Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"NEVERMIND, the problem is already fixed: proactively detecting and troubleshooting customer DSL problems\",\"authors\":\"Yu Jin, N. Duffield, Alexandre Gerber, P. Haffner, S. Sen, Zhi-Li Zhang\",\"doi\":\"10.1145/1921168.1921178\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditional DSL troubleshooting solutions are reactive, relying mainly on customers to report problems, and tend to be labor-intensive, time consuming, prone to incorrect resolutions and overall can contribute to increased customer dissatisfaction. In this paper, we propose a proactive approach to facilitate troubleshooting customer edge problems and reducing customer tickets. Our system consists of: i) a ticket predictor which predicts future customer tickets; and ii) a trouble locator which helps technicians accelerate the troubleshooting process during field dispatches. Both components infer future tickets and trouble locations based on existing sparse line measurements, and the inference models are constructed automatically using supervised machine learning techniques. We propose several novel techniques to address the operational constraints in DSL networks and to enhance the accuracy of NEVERMIND. Extensive evaluations using an entire year worth of customer tickets and measurement data from a large network show that our method can predict thousands of future customer tickets per week with high accuracy and signifcantly reduce the time and effort for diagnosing these tickets. This is benefcial as it has the effect of both reducing the number of customer care calls and improving customer satisfaction.\",\"PeriodicalId\":20688,\"journal\":{\"name\":\"Proceedings of The 6th International Conference on Innovation in Science and Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of The 6th International Conference on Innovation in Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1921168.1921178\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of The 6th International Conference on Innovation in Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1921168.1921178","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 24

摘要

传统的DSL故障排除解决方案是被动的,主要依赖于客户报告问题,并且往往是劳动密集型的,耗时的,容易出现错误的解决方案,并且总的来说可能会增加客户的不满。在本文中,我们提出了一种主动的方法来方便地排除客户边缘问题并减少客户票。我们的系统包括:i)预测未来客户门票的门票预测器;ii)故障定位器,帮助技术人员在现场调度过程中加快故障排除过程。这两个组件都基于现有的稀疏线测量来推断未来的票据和故障位置,并且使用监督机器学习技术自动构建推理模型。我们提出了几种新的技术来解决DSL网络中的操作限制,并提高NEVERMIND的准确性。使用一整年的客户票和来自大型网络的测量数据进行的广泛评估表明,我们的方法可以每周高精度地预测数千张未来的客户票,并显着减少诊断这些票的时间和精力。这是有益的,因为它既可以减少客户服务电话的数量,又可以提高客户满意度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
NEVERMIND, the problem is already fixed: proactively detecting and troubleshooting customer DSL problems
Traditional DSL troubleshooting solutions are reactive, relying mainly on customers to report problems, and tend to be labor-intensive, time consuming, prone to incorrect resolutions and overall can contribute to increased customer dissatisfaction. In this paper, we propose a proactive approach to facilitate troubleshooting customer edge problems and reducing customer tickets. Our system consists of: i) a ticket predictor which predicts future customer tickets; and ii) a trouble locator which helps technicians accelerate the troubleshooting process during field dispatches. Both components infer future tickets and trouble locations based on existing sparse line measurements, and the inference models are constructed automatically using supervised machine learning techniques. We propose several novel techniques to address the operational constraints in DSL networks and to enhance the accuracy of NEVERMIND. Extensive evaluations using an entire year worth of customer tickets and measurement data from a large network show that our method can predict thousands of future customer tickets per week with high accuracy and signifcantly reduce the time and effort for diagnosing these tickets. This is benefcial as it has the effect of both reducing the number of customer care calls and improving customer satisfaction.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信