连续集成、流处理和企业数据操作中的连续元数据

IF 1.3 3区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Data Intelligence Pub Date : 2023-02-22 DOI:10.1162/dint_a_00193
M. Underwood
{"title":"连续集成、流处理和企业数据操作中的连续元数据","authors":"M. Underwood","doi":"10.1162/dint_a_00193","DOIUrl":null,"url":null,"abstract":"ABSTRACT Implementations of metadata tend to favor centralized, static metadata. This depiction is at variance with the past decade of focus on big data, cloud native architectures and streaming platforms. Big data velocity can demand a correspondingly dynamic view of metadata. These trends, which include DevOps, CI/CD, DataOps and data fabric, are surveyed. Several specific cloud native tools are reviewed and weaknesses in their current metadata use are identified. Implementations are suggested which better exploit capabilities of streaming platform paradigms, in which metadata is continuously collected in dynamic contexts. Future cloud native software features are identified which could enable streamed metadata to power real time data fusion or fine tune automated reasoning through real time ontology updates.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":"5 1","pages":"275-288"},"PeriodicalIF":1.3000,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Continuous Metadata in Continuous Integration, Stream Processing and Enterprise DataOps\",\"authors\":\"M. Underwood\",\"doi\":\"10.1162/dint_a_00193\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Implementations of metadata tend to favor centralized, static metadata. This depiction is at variance with the past decade of focus on big data, cloud native architectures and streaming platforms. Big data velocity can demand a correspondingly dynamic view of metadata. These trends, which include DevOps, CI/CD, DataOps and data fabric, are surveyed. Several specific cloud native tools are reviewed and weaknesses in their current metadata use are identified. Implementations are suggested which better exploit capabilities of streaming platform paradigms, in which metadata is continuously collected in dynamic contexts. Future cloud native software features are identified which could enable streamed metadata to power real time data fusion or fine tune automated reasoning through real time ontology updates.\",\"PeriodicalId\":34023,\"journal\":{\"name\":\"Data Intelligence\",\"volume\":\"5 1\",\"pages\":\"275-288\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2023-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1162/dint_a_00193\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Intelligence","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1162/dint_a_00193","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 1

摘要

摘要元数据的实现倾向于支持集中式的静态元数据。这种描述与过去十年对大数据、云原生架构和流媒体平台的关注不一致。大数据速度可能需要相应的元数据动态视图。调查了这些趋势,包括DevOps、CI/CD、DataOps和数据结构。审查了几个特定的云原生工具,并确定了它们当前元数据使用中的弱点。提出了更好地利用流媒体平台范式的功能的实现,其中元数据在动态上下文中不断收集。确定了未来的云原生软件功能,这些功能可以使流式元数据能够支持实时数据融合或通过实时本体更新微调自动推理。
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
Continuous Metadata in Continuous Integration, Stream Processing and Enterprise DataOps
ABSTRACT Implementations of metadata tend to favor centralized, static metadata. This depiction is at variance with the past decade of focus on big data, cloud native architectures and streaming platforms. Big data velocity can demand a correspondingly dynamic view of metadata. These trends, which include DevOps, CI/CD, DataOps and data fabric, are surveyed. Several specific cloud native tools are reviewed and weaknesses in their current metadata use are identified. Implementations are suggested which better exploit capabilities of streaming platform paradigms, in which metadata is continuously collected in dynamic contexts. Future cloud native software features are identified which could enable streamed metadata to power real time data fusion or fine tune automated reasoning through real time ontology updates.
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
Data Intelligence
Data Intelligence COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
6.50
自引率
15.40%
发文量
40
审稿时长
8 weeks
期刊最新文献
The Limitations and Ethical Considerations of ChatGPT Rule Mining Trends from 1987 to 2022: A Bibliometric Analysis and Visualization Classification and quantification of timestamp data quality issues and its impact on data quality outcome BIKAS: Bio-Inspired Knowledge Acquisition and Simulacrum—A Knowledge Database to Support Multifunctional Design Concept Generation Exploring Attentive Siamese LSTM for Low-Resource Text Plagiarism Detection
×
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