iORDER: Mining Implicit Domain Orders

Alexander Bianchi, Reza Karegar, P. Godfrey, Lukasz Golab, M. Kargar, D. Srivastava, Jaroslaw Szlichta
{"title":"iORDER: Mining Implicit Domain Orders","authors":"Alexander Bianchi, Reza Karegar, P. Godfrey, Lukasz Golab, M. Kargar, D. Srivastava, Jaroslaw Szlichta","doi":"10.1109/ICDE55515.2023.00283","DOIUrl":null,"url":null,"abstract":"In this demonstration paper, we describe iORDER, a tool that identifies implicit domain orders in data, such as Small Medium Large. iORDER extends the machinery of order dependency discovery to identify and rank interesting orders. Using real-world data, we showcase how implicit orders help users interpret the semantics of ordered data, how to interactively validate implicit orders to aid in the discovery process, and how to apply implicit orders to applications including data profiling, data mining and knowledge bases.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"243 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE55515.2023.00283","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this demonstration paper, we describe iORDER, a tool that identifies implicit domain orders in data, such as Small Medium Large. iORDER extends the machinery of order dependency discovery to identify and rank interesting orders. Using real-world data, we showcase how implicit orders help users interpret the semantics of ordered data, how to interactively validate implicit orders to aid in the discovery process, and how to apply implicit orders to applications including data profiling, data mining and knowledge bases.
iORDER:挖掘隐式领域顺序
在这篇演示论文中,我们描述了iORDER,一个识别数据中隐式领域顺序的工具,例如Small Medium Large。iORDER扩展了订单依赖项发现机制,以识别感兴趣的订单并对其进行排序。使用真实世界的数据,我们展示了隐式顺序如何帮助用户解释有序数据的语义,如何交互式地验证隐式顺序以帮助发现过程,以及如何将隐式顺序应用于包括数据分析、数据挖掘和知识库在内的应用程序。
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