Integrating data mining with case based reasoning (CBR) to improve the proactivity of pervasive applications

Nesrine Gouttaya, Ahlame Begdouri
{"title":"Integrating data mining with case based reasoning (CBR) to improve the proactivity of pervasive applications","authors":"Nesrine Gouttaya, Ahlame Begdouri","doi":"10.1109/CIST.2012.6388077","DOIUrl":null,"url":null,"abstract":"Current context-aware adaptation techniques in smart environments are limited in their support for proactivity and user personalization. A reliance on developer modification and an inability to automatically learn from user interactions hinder their use for providing proactive services that can be adapted to the frequent changes of the context of individuals. To address these problems we propose a proactive and personalized approach to adaptation. Our approach integrates both Case-based Reasoning (CBR) and data mining techniques. It is based on CBR, but aided by data mining to extract user patterns and knowledge adaptation from users' interaction history.","PeriodicalId":120664,"journal":{"name":"2012 Colloquium in Information Science and Technology","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Colloquium in Information Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIST.2012.6388077","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

Current context-aware adaptation techniques in smart environments are limited in their support for proactivity and user personalization. A reliance on developer modification and an inability to automatically learn from user interactions hinder their use for providing proactive services that can be adapted to the frequent changes of the context of individuals. To address these problems we propose a proactive and personalized approach to adaptation. Our approach integrates both Case-based Reasoning (CBR) and data mining techniques. It is based on CBR, but aided by data mining to extract user patterns and knowledge adaptation from users' interaction history.
将数据挖掘与基于案例的推理(CBR)相结合,提高普适应用程序的主动性
当前智能环境中的上下文感知适应技术在支持主动性和用户个性化方面受到限制。对开发人员修改的依赖以及无法从用户交互中自动学习,阻碍了它们用于提供可适应个人环境频繁变化的主动服务。为了解决这些问题,我们提出了一种主动和个性化的适应方法。我们的方法集成了基于案例的推理(CBR)和数据挖掘技术。它以CBR为基础,辅以数据挖掘,从用户的交互历史中提取用户模式和知识适应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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