信息表征的操作

ACM-SE 35 Pub Date : 1997-04-02 DOI:10.1145/2817460.2817534
P. Maine, Xia Ping
{"title":"信息表征的操作","authors":"P. Maine, Xia Ping","doi":"10.1145/2817460.2817534","DOIUrl":null,"url":null,"abstract":"Information Representations, IR, are linear representations of topological maps that can be used to describe virtually any abstract, static or dynamic data. An IR can be manipulated to obtain mathematically equivalent forms that are either unique (normal) or alternative (canonical) representations.\n This concise paper draws attention to the recently developed high-speed chain-tracing algorithms for obtaining normal and canonical forms of IR's. Copies of the submitted full length paper will be available at the conference.","PeriodicalId":274966,"journal":{"name":"ACM-SE 35","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Manipulation of information representations\",\"authors\":\"P. Maine, Xia Ping\",\"doi\":\"10.1145/2817460.2817534\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Information Representations, IR, are linear representations of topological maps that can be used to describe virtually any abstract, static or dynamic data. An IR can be manipulated to obtain mathematically equivalent forms that are either unique (normal) or alternative (canonical) representations.\\n This concise paper draws attention to the recently developed high-speed chain-tracing algorithms for obtaining normal and canonical forms of IR's. Copies of the submitted full length paper will be available at the conference.\",\"PeriodicalId\":274966,\"journal\":{\"name\":\"ACM-SE 35\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-04-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM-SE 35\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2817460.2817534\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM-SE 35","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2817460.2817534","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

信息表示(IR)是拓扑映射的线性表示,可以用来描述几乎任何抽象的、静态的或动态的数据。可以对IR进行操作,以获得数学上等价的形式,这些形式要么是唯一的(正规的),要么是可选的(规范的)表示。这篇简明的文章引起了人们对最近发展的高速链跟踪算法的注意,这些算法用于获得IR的正规和规范形式。提交的论文全文将在会议上提供。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Manipulation of information representations
Information Representations, IR, are linear representations of topological maps that can be used to describe virtually any abstract, static or dynamic data. An IR can be manipulated to obtain mathematically equivalent forms that are either unique (normal) or alternative (canonical) representations. This concise paper draws attention to the recently developed high-speed chain-tracing algorithms for obtaining normal and canonical forms of IR's. Copies of the submitted full length paper will be available at the conference.
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
期刊最新文献
Improving the identification of verbs Constructing Delaunay triangulation on the Intel Paragon Software agents and the role of market protocols Interactive Petri net simulation Hybrid evolutionary path planning via visibility-based repair
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1