{"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}
引用次数: 3
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.