间歇泉的演示:来源提取和数据科学脚本的应用

Fotis Psallidas, Megan Leszczynski, M. Namaki, A. Floratou, Ashvin Agrawal, Konstantinos Karanasos, Subru Krishnan, Pavle Subotic, Markus Weimer, Yinghui Wu, Yiwen Zhu
{"title":"间歇泉的演示:来源提取和数据科学脚本的应用","authors":"Fotis Psallidas, Megan Leszczynski, M. Namaki, A. Floratou, Ashvin Agrawal, Konstantinos Karanasos, Subru Krishnan, Pavle Subotic, Markus Weimer, Yinghui Wu, Yiwen Zhu","doi":"10.1145/3555041.3589717","DOIUrl":null,"url":null,"abstract":"As enterprises have started developing and deploying complicated data science workloads at scale, the need for mechanisms that enable enterprise-grade data science (e.g., compliance or auditing) has become more pronounced. In this paper, we present Geyser, an extensible provenance system for data science workloads that can be used as a foundation for enterprise-grade data science. Our system supports both static and dynamic provenance, over a wide range of data science scripts, driven by a knowledge base of data science APIs. We demonstrate the wide applicability of the system using various industrial applications: provenance extraction, model compliance, model linting, model versioning, and poisoning detection. A video of the demonstration is available at https://aka.ms/geyserdemo.","PeriodicalId":161812,"journal":{"name":"Companion of the 2023 International Conference on Management of Data","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Demonstration of Geyser: Provenance Extraction and Applications over Data Science Scripts\",\"authors\":\"Fotis Psallidas, Megan Leszczynski, M. Namaki, A. Floratou, Ashvin Agrawal, Konstantinos Karanasos, Subru Krishnan, Pavle Subotic, Markus Weimer, Yinghui Wu, Yiwen Zhu\",\"doi\":\"10.1145/3555041.3589717\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As enterprises have started developing and deploying complicated data science workloads at scale, the need for mechanisms that enable enterprise-grade data science (e.g., compliance or auditing) has become more pronounced. In this paper, we present Geyser, an extensible provenance system for data science workloads that can be used as a foundation for enterprise-grade data science. Our system supports both static and dynamic provenance, over a wide range of data science scripts, driven by a knowledge base of data science APIs. We demonstrate the wide applicability of the system using various industrial applications: provenance extraction, model compliance, model linting, model versioning, and poisoning detection. A video of the demonstration is available at https://aka.ms/geyserdemo.\",\"PeriodicalId\":161812,\"journal\":{\"name\":\"Companion of the 2023 International Conference on Management of Data\",\"volume\":\"54 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Companion of the 2023 International Conference on Management of Data\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3555041.3589717\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Companion of the 2023 International Conference on Management of Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3555041.3589717","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

随着企业开始大规模开发和部署复杂的数据科学工作负载,对支持企业级数据科学(例如,遵从性或审计)的机制的需求变得更加明显。在本文中,我们介绍了Geyser,这是一个可扩展的数据科学工作负载溯源系统,可以用作企业级数据科学的基础。我们的系统支持静态和动态来源,通过广泛的数据科学脚本,由数据科学api知识库驱动。我们通过各种工业应用演示了该系统的广泛适用性:来源提取、模型遵从性、模型检测、模型版本控制和中毒检测。该演示的视频可在https://aka.ms/geyserdemo上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Demonstration of Geyser: Provenance Extraction and Applications over Data Science Scripts
As enterprises have started developing and deploying complicated data science workloads at scale, the need for mechanisms that enable enterprise-grade data science (e.g., compliance or auditing) has become more pronounced. In this paper, we present Geyser, an extensible provenance system for data science workloads that can be used as a foundation for enterprise-grade data science. Our system supports both static and dynamic provenance, over a wide range of data science scripts, driven by a knowledge base of data science APIs. We demonstrate the wide applicability of the system using various industrial applications: provenance extraction, model compliance, model linting, model versioning, and poisoning detection. A video of the demonstration is available at https://aka.ms/geyserdemo.
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
×
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:604180095
Book学术官方微信