Data-centric Artificial Intelligence: A Survey

IF 23.8 1区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

ACM Computing Surveys Pub Date : 2025-01-06 DOI:10.1145/3711118

Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, Xia Hu

{"title":"Data-centric Artificial Intelligence: A Survey","authors":"Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, Xia Hu","doi":"10.1145/3711118","DOIUrl":null,"url":null,"abstract":"Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for building machine learning models. Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI . The attention of researchers and practitioners has gradually shifted from advancing model design to enhancing the quality and quantity of the data. In this survey, we discuss the necessity of data-centric AI, followed by a holistic view of three general data-centric goals (training data development, inference data development, and data maintenance) and the representative methods. We also organize the existing literature from automation and collaboration perspectives, discuss the challenges, and tabulate the benchmarks for various tasks. We believe this is the first comprehensive survey that provides a global view of a spectrum of tasks across various stages of the data lifecycle. We hope it can help the readers efficiently grasp a broad picture of this field, and equip them with the techniques and further research ideas to systematically engineer data for building AI systems. A companion list of data-centric AI resources will be regularly updated on https://github.com/daochenzha/data-centric-AI","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"55 1","pages":""},"PeriodicalIF":23.8000,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Computing Surveys","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3711118","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}

引用次数: 0

Abstract

Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for building machine learning models. Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI . The attention of researchers and practitioners has gradually shifted from advancing model design to enhancing the quality and quantity of the data. In this survey, we discuss the necessity of data-centric AI, followed by a holistic view of three general data-centric goals (training data development, inference data development, and data maintenance) and the representative methods. We also organize the existing literature from automation and collaboration perspectives, discuss the challenges, and tabulate the benchmarks for various tasks. We believe this is the first comprehensive survey that provides a global view of a spectrum of tasks across various stages of the data lifecycle. We hope it can help the readers efficiently grasp a broad picture of this field, and equip them with the techniques and further research ideas to systematically engineer data for building AI systems. A companion list of data-centric AI resources will be regularly updated on https://github.com/daochenzha/data-centric-AI

查看原文本刊更多论文

以数据为中心的人工智能：调查

人工智能（AI）正在几乎每个领域产生深远的影响。它取得巨大成功的一个重要因素是可以获得大量高质量的数据来构建机器学习模型。最近，数据在人工智能中的作用被显著放大，从而产生了以数据为中心的人工智能概念。研究者和实践者的关注点逐渐从推进模型设计转向提高数据的质量和数量。在本调查中，我们讨论了以数据为中心的人工智能的必要性，然后对三个一般以数据为中心的目标（训练数据开发、推理数据开发和数据维护）和代表性方法进行了全面的看法。我们还从自动化和协作的角度组织了现有的文献，讨论了挑战，并列出了各种任务的基准。我们相信这是第一个全面的调查，它提供了跨数据生命周期各个阶段的任务范围的全局视图。我们希望它能帮助读者有效地掌握这一领域的广阔图景，并为他们提供技术和进一步的研究思路，以便系统地设计数据以构建人工智能系统。以数据为中心的人工智能资源的配套列表将在https://github.com/daochenzha/data-centric-AI上定期更新

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

ACM Computing Surveys 工程技术-计算机：理论方法

CiteScore

33.20

自引率

0.60%

发文量

372

审稿时长

12 months

期刊介绍： ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.