基于风车法的通信网络稀疏事件求解支持

D. Ferro, C. Jonker, A. Salden
{"title":"基于风车法的通信网络稀疏事件求解支持","authors":"D. Ferro, C. Jonker, A. Salden","doi":"10.1109/CASoN.2009.17","DOIUrl":null,"url":null,"abstract":"This paper introduces the Windmill method for constructing situation sensitive communication support systems for organizations consisting of a network of autonomous professionals involved in standard duties encountering occasional incidents of a time-critical nature for which they have to call for help. The Windmill method is based on statistical data filtering techniques for ranking available resources to handle incident according to their availability, location, skills and experience. It is especially useful for domains in which the human workforce changes over time and incidents are relatively sparse with respect to location and frequency of occurrence.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"The Windmill Method for Setting up Support for Resolving Sparse Incidents in Communication Networks\",\"authors\":\"D. Ferro, C. Jonker, A. Salden\",\"doi\":\"10.1109/CASoN.2009.17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces the Windmill method for constructing situation sensitive communication support systems for organizations consisting of a network of autonomous professionals involved in standard duties encountering occasional incidents of a time-critical nature for which they have to call for help. The Windmill method is based on statistical data filtering techniques for ranking available resources to handle incident according to their availability, location, skills and experience. It is especially useful for domains in which the human workforce changes over time and incidents are relatively sparse with respect to location and frequency of occurrence.\",\"PeriodicalId\":425748,\"journal\":{\"name\":\"2009 International Conference on Computational Aspects of Social Networks\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Computational Aspects of Social Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CASoN.2009.17\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Computational Aspects of Social Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CASoN.2009.17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

本文介绍了风车方法,用于为参与标准职责的自主专业人员组成的网络组成的组织构建情况敏感通信支持系统,这些组织遇到了时间关键性质的偶然事件,他们不得不寻求帮助。Windmill方法基于统计数据过滤技术,根据可用性、位置、技能和经验对可用资源进行排序,以处理事件。它对于人力资源随时间变化的领域特别有用,并且相对于发生的位置和频率而言,事件相对较少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Windmill Method for Setting up Support for Resolving Sparse Incidents in Communication Networks
This paper introduces the Windmill method for constructing situation sensitive communication support systems for organizations consisting of a network of autonomous professionals involved in standard duties encountering occasional incidents of a time-critical nature for which they have to call for help. The Windmill method is based on statistical data filtering techniques for ranking available resources to handle incident according to their availability, location, skills and experience. It is especially useful for domains in which the human workforce changes over time and incidents are relatively sparse with respect to location and frequency of occurrence.
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