Provenance: On and Behind the Screens

Melanie Herschel, Marcel Hlawatsch
{"title":"Provenance: On and Behind the Screens","authors":"Melanie Herschel, Marcel Hlawatsch","doi":"10.1145/2882903.2912568","DOIUrl":null,"url":null,"abstract":"Collecting and processing provenance, i.e., information describing the production process of some end product, is important in various applications, e.g., to assess quality, to ensure reproducibility, or to reinforce trust in the end product. In the past, different types of provenance meta-data have been proposed, each with a different scope. The first part of the proposed tutorial provides an overview and comparison of these different types of provenance. To put provenance to good use, it is essential to be able to interact with and present provenance data in a user-friendly way. Often, users interested in provenance are not necessarily experts in databases or query languages, as they are typically domain experts of the product and production process for which provenance is collected (biologists, journalists, etc.). Furthermore, in some scenarios, it is difficult to use solely queries for analyzing and exploring provenance data. The second part of this tutorial therefore focuses on enabling users to leverage provenance through adapted visualizations. To this end, we will present some fundamental concepts of visualization before we discuss possible visualizations for provenance.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"112 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2016 International Conference on Management of Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2882903.2912568","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 19

Abstract

Collecting and processing provenance, i.e., information describing the production process of some end product, is important in various applications, e.g., to assess quality, to ensure reproducibility, or to reinforce trust in the end product. In the past, different types of provenance meta-data have been proposed, each with a different scope. The first part of the proposed tutorial provides an overview and comparison of these different types of provenance. To put provenance to good use, it is essential to be able to interact with and present provenance data in a user-friendly way. Often, users interested in provenance are not necessarily experts in databases or query languages, as they are typically domain experts of the product and production process for which provenance is collected (biologists, journalists, etc.). Furthermore, in some scenarios, it is difficult to use solely queries for analyzing and exploring provenance data. The second part of this tutorial therefore focuses on enabling users to leverage provenance through adapted visualizations. To this end, we will present some fundamental concepts of visualization before we discuss possible visualizations for provenance.
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
出处:屏幕上和屏幕后
收集和处理来源,即描述某些最终产品的生产过程的信息,在各种应用中都很重要,例如,评估质量,确保再现性,或加强对最终产品的信任。在过去,人们提出了不同类型的来源元数据,每种类型都有不同的范围。建议教程的第一部分提供了这些不同类型的来源的概述和比较。为了充分利用出处,必须能够以用户友好的方式与出处数据进行交互并显示出处数据。通常,对来源感兴趣的用户不一定是数据库或查询语言方面的专家,因为他们通常是收集来源的产品和生产过程的领域专家(生物学家、记者等)。此外,在某些情况下,很难单独使用查询来分析和探索来源数据。因此,本教程的第二部分侧重于使用户能够通过适应的可视化来利用出处。为此,在讨论可能的来源可视化之前,我们将介绍一些可视化的基本概念。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
An Experimental Comparison of Thirteen Relational Equi-Joins in Main Memory Rheem: Enabling Multi-Platform Task Execution Wander Join: Online Aggregation for Joins Graph Summarization for Geo-correlated Trends Detection in Social Networks Emma in Action: Declarative Dataflows for Scalable Data Analysis
×
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