Awesome-Data-Centric-AI

A curated, but incomplete, list of data-centric AI resources. It should be noted that it is unfeasible to encompass every paper. Thus, we prefer to selectively choose papers that present a range of distinct ideas. We welcome contributions to further enrich and refine this list.

If you want to contribute to this list, please feel free to send a pull request. Also, you can contact [email protected].

• Survey paper: Data-centric Artificial Intelligence: A Survey
• Perspective paper (SDM 2023): Data-centric AI: Perspectives and Challenges
• Data-centric AI: Techniques and Future Perspectives (KDD 2023 Tutorial): [Website] [Slides] [Video] [Paper]
• Blogs:
  • What Are the Data-Centric AI Concepts behind GPT Models?
  • Are Prompts Generated by Large Language Models (LLMs) Reliable?
  • The Data-centric AI Concepts in Segment Anything
• 中文解读:
  • GPT模型成功的背后用到了哪些以数据为中心的人工智能（Data-centric AI）技术？
  • 如何评价Meta/FAIR 最新工作Segment Anything？
  • 进行data-centric的研究时，需要的算力大吗？
• Graph Structure Learning (GSL) is a data-centric AI direction in graph neural networks (GNNs):
  • Paper: OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
  • Code: https://github.com/OpenGSL/OpenGSL
  • 中文解读：GNN中的Data-centric AI —— 图结构学习（GSL）以及基准库OpenGSL介绍
• Check our latest Knowledge Graphs (KGs) based paper search engine DiscoverPath: https://github.com/ynchuang/DiscoverPath

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What is Data-centric AI?

Data-centric AI is an emerging field that focuses on engineering data to improve AI systems with enhanced data quality and quantity.

Data-centric AI vs. Model-centric AI

In the conventional model-centric AI lifecycle, researchers and developers primarily focus on identifying more effective models to improve AI performance while keeping the data largely unchanged. However, this model-centric paradigm overlooks the potential quality issues and undesirable flaws of data, such as missing values, incorrect labels, and anomalies. Complementing the existing efforts in model advancement, data-centric AI emphasizes the systematic engineering of data to build AI systems, shifting our focus from model to data.

It is important to note that "data-centric" differs fundamentally from "data-driven", as the latter only emphasizes the use of data to guide AI development, which typically still centers on developing models rather than engineering data.

Why Data-centric AI?

Two motivating examples of GPT models highlight the central role of data in AI.

• On the left, large and high-quality training data are the driving force of recent successes of GPT models, while model architectures remain similar, except for more model weights.
• On the right, when the model becomes sufficiently powerful, we only need to engineer prompts (inference data) to accomplish our objectives, with the model being fixed.

Another example is Segment Anything, a foundation model for computer vision. The core of training Segment Anything lies in the large amount of annotated data, containing more than 1 billion masks, which is 400 times larger than existing segmentation datasets.

What is the Data-centric AI Framework?

Data-centric AI framework consists of three goals: training data development, inference data development, and data maintenance, where each goal is associated with several sub-goals.

• The goal of training data development is to collect and produce rich and high-quality training data to support the training of machine learning models.
• The objective of inference data development is to create novel evaluation sets that can provide more granular insights into the model or trigger a specific capability of the model with engineered data inputs.
• The purpose of data maintenance is to ensure the quality and reliability of data in a dynamic environment.

Cite this Work

Zha, Daochen, et al. "Data-centric Artificial Intelligence: A Survey." arXiv preprint arXiv:2303.10158, 2023.

@article{zha2023data-centric-survey,
  title={Data-centric Artificial Intelligence: A Survey},
  author={Zha, Daochen and Bhat, Zaid Pervaiz and Lai, Kwei-Herng and Yang, Fan and Jiang, Zhimeng and Zhong, Shaochen and Hu, Xia},
  journal={arXiv preprint arXiv:2303.10158},
  year={2023}
}

Zha, Daochen, et al. "Data-centric AI: Perspectives and Challenges." SDM, 2023.

@inproceedings{zha2023data-centric-perspectives,
  title={Data-centric AI: Perspectives and Challenges},
  author={Zha, Daochen and Bhat, Zaid Pervaiz and Lai, Kwei-Herng and Yang, Fan and Hu, Xia},
  booktitle={SDM},
  year={2023}
}

Table of Contents

• Training Data Development
  • Data Collection
  • Data Labeling
  • Data Preparation
  • Data Reduction
  • Data Augmentation
  • Pipeline Search
• Inference Data Development
  • In-distribution Evaluation
  • Out-of-distribution Evaluation
  • Prompt Engineering
• Data Maintenance
  • Data Understanding
  • Data Quality Assurance
  • Data Storage and Retrieval
• Data Benchmark
  • Training Data Development Benchmark
  • Inference Data Development Benchmark
  • Data Maintenance Benchmark
  • Unified Benchmark

Training Data Development

Data Collection

• Revisiting time series outlier detection: Definitions and benchmarks, NeurIPS 2021 [Paper] [Code]
• Dataset discovery in data lakes, ICDE 2020 [Paper]
• Aurum: A data discovery system, ICDE 2018 [Paper] [Code]
• Table union search on open data, VLDB 2018 [Paper]
• Data Integration: The Current Status and the Way Forward, IEEE Computer Society Technical Committee on Data Engineering 2018 [Paper]
• To join or not to join? thinking twice about joins before feature selection, SIGMOD 2016 [Paper]
• Data curation at scale: the data tamer system, CIDR 2013 [Paper]
• Data integration: A theoretical perspective, PODS 2002 [Paper]

Data Labeling

• Segment Anything [Paper] [code]
• Active Ensemble Learning for Knowledge Graph Error Detection, WSDM 2023 [Paper]
• Active-Learning-as-a-Service: An Efficient MLOps System for Data-Centric AI, NeurIPS 2022 Workshop on Human in the Loop Learning [paper] [code]
• Training language models to follow instructions with human feedback, NeurIPS 2022 [Paper]
• Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling, ICLR 2021 [Paper] [Code]
• A survey of deep active learning, ACM Computing Surveys 2021 [Paper]
• Adaptive rule discovery for labeling text data, SIGMOD 2021 [Paper]
• Cut out the annotator, keep the cutout: better segmentation with weak supervision, ICLR 2021 [Paper]
• Meta-AAD: Active anomaly detection with deep reinforcement learning, ICDM 2020 [Paper] [Code]
• Snorkel: Rapid training data creation with weak supervision, VLDB 2020 [Paper] [Code]
• Graph-based semi-supervised learning: A review, Neurocomputing 2020 [Paper]
• Annotator rationales for labeling tasks in crowdsourcing, JAIR 2020 [Paper]
• Rethinking pre-training and self-training, NeurIPS 2020 [Paper]
• Multi-label dataless text classification with topic modeling, KIS 2019 [Paper]
• Data programming: Creating large training sets, quickly, NeurIPS 2016 [Paper]
• Semi-supervised consensus labeling for crowdsourcing, SIGIR 2011 [Paper]
• Vox Populi: Collecting High-Quality Labels from a Crowd, COLT 2009 [Paper]
• Democratic co-learning, ICTAI 2004 [Paper]
• Active learning with statistical models, JAIR 1996 [Paper]

Data Preparation

• DataFix: Adversarial Learning for Feature Shift Detection and Correction, NeurIPS 2023 [Paper] [Code]
• OpenGSL: A Comprehensive Benchmark for Graph Structure Learning, arXiv 2023 [Paper] [Code]
• TSFEL: Time series feature extraction library, SoftwareX 2020 [Paper] [Code]
• Alphaclean: Automatic generation of data cleaning pipelines, arXiv 2019 [Paper] [Code]
• Introduction to Scikit-learn, Book 2019 [Paper] [Code]
• Feature extraction: a survey of the types, techniques, applications, ICSC 2019 [Paper]
• Feature engineering for predictive modeling using reinforcement learning, AAAI 2018 [Paper]
• Time series classification from scratch with deep neural networks: A strong baseline, IIJCNN 2017 [Paper]
• Missing data imputation: focusing on single imputation, ATM 2016 [Paper]
• Estimating the number and sizes of fuzzy-duplicate clusters, CIKM 2014 [Paper]
• Data normalization and standardization: a technical report, MLTR 2014 [Paper]
• CrowdER: crowdsourcing entity resolution, VLDB 2012 [Paper]
• Imputation of Missing Data Using Machine Learning Techniques, KDD 1996 [Paper]

Data Reduction

• Active feature selection for the mutual information criterion, AAAI 2021 [Paper] [Code]
• Active incremental feature selection using a fuzzy-rough-set-based information entropy, IEEE Transactions on Fuzzy Systems, 2020 [Paper]
• MESA: boost ensemble imbalanced learning with meta-sampler, NeurIPS 2020 [Paper] [Code]
• Autoencoders, arXiv 2020 [Paper]
• Feature selection: A data perspective, ACM COmputer Surveys, 2017 [Paper] [Code]
• Intrusion detection model using fusion of chi-square feature selection and multi class SVM, Journal of King Saud University-Computer and Information Sciences 2017 [Paper]
• Feature selection and analysis on correlated gas sensor data with recursive feature elimination, Sensors and Actuators B: Chemical 2015 [Paper]
• Embedded unsupervised feature selection, AAAI 2015 [Paper]
• Using random undersampling to alleviate class imbalance on tweet sentiment data, ICIRI 2015 [Paper]
• Feature selection based on information gain, IJITEE 2013 [Paper]
• Linear discriminant analysis, Book 2013 [Paper]
• Introduction to k nearest neighbour classification and condensed nearest neighbour data reduction, 2012 [Paper]
• Principal component analysis, Wiley Interdisciplinary Reviews 2010 [Paper] [Code]
• Finding representative patterns with ordered projections, Pattern Recognition 2003 [Paper]

Data Augmentation

• Towards automated imbalanced learning with deep hierarchical reinforcement learning, CIKM 2022 [Paper] [Code]
• G-Mixup: Graph Data Augmentation for Graph Classification, ICML 2022 [Paper] [Code]
• Cascaded Diffusion Models for High Fidelity Image Generation, JMLR 2022 [Paper]
• Time series data augmentation for deep learning: A survey, IJCAI 2021 [Paper]
• Text data augmentation for deep learning, JBD 2020 [Paper]
• Mixtext: Linguistically-informed interpolation of hidden space for semi-supervised text classification, ACL 2020 [Paper] [Code]
• Autoaugment: Learning augmentation policies from data, CVPR 2019 [Paper] [Code]
• Mixup: Beyond empirical risk minimization, ICLR 2018 [Paper] [Code]
• Synthetic data augmentation using GAN for improved liver lesion classification, ISBI 2018 [Paper] [Code]
• Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation, ASRU 2017 [Paper]
• Character-level convolutional networks for text classification, NeurIPS 2015 [Paper] [Code]
• ADASYN: Adaptive synthetic sampling approach for imbalanced learning, IJCNN 2008 [Paper] [Code]
• SMOTE: synthetic minority over-sampling technique, JAIR 2002 [Paper] [Code]

Pipeline Search

• Towards Personalized Preprocessing Pipeline Search, arXiv 2023 [Paper]
• AutoVideo: An Automated Video Action Recognition System, IJCAI 2022 [Paper] [Code]
• Tods: An automated time series outlier detection system, AAAI 2021 [Paper] [Code]
• Deepline: Automl tool for pipelines generation using deep reinforcement learning and hierarchical actions filtering, KDD 2020 [Paper]
• On evaluation of automl systems, ICML 2020 [Paper]
• AlphaD3M: Machine learning pipeline synthesis, ICML 2018 [Paper]
• Efficient and robust automated machine learning, NeurIPS 2015 [Paper] [Code]
• Tiny3D: A Data-Centric AI based 3D Object Detection Service Production System [Code]
• Learning From How Humans Correct [Paper]
• The Re-Label Method For Data-Centric Machine Learning [Paper]

Inference Data Development

In-distribution Evaluation

• FOCUS: Flexible optimizable counterfactual explanations for tree ensembles, AAAI 2022 [Paper] [Code]
• Sliceline: Fast, linear-algebra-based slice finding for ml model debugging, SIGMOD 2021 [Paper] [Code]
• Counterfactual explanations for oblique decision trees: Exact, efficient algorithms, AAAI 2021 [Paper]
• A Step Towards Global Counterfactual Explanations: Approximating the Feature Space Through Hierarchical Division and Graph Search, AAIML 2021 [Paper]
• An exact counterfactual-example-based approach to tree-ensemble models interpretability, arXiv 2021 [Paper] [Code]
• No subclass left behind: Fine-grained robustness in coarse-grained classification problems, NeurIPS 2020 [Paper] [Code]
• FACE: feasible and actionable counterfactual explanations, AIES 2020 [Paper] [Code]
• DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization, IJCAI 2020 [Paper]
• Multi-objective counterfactual explanations, arXiv 2020 [Paper] [Code]
• Certifai: Counterfactual explanations for robustness, transparency, interpretability, and fairness of artificial intelligence models, AIES 2020 [Paper] [Code]
• Propublica's compas data revisited, arXiv 2019 [Paper]
• Slice finder: Automated data slicing for model validation, ICDE 2019 [Paper] [Code]
• Multiaccuracy: Black-box post-processing for fairness in classification, AIES 2019 [Paper] [Code]
• Model agnostic contrastive explanations for structured data, arXiv 2019 [Paper]
• Counterfactual explanations without opening the black box: Automated decisions and the GDPR, Harvard Journal of Law & Technology 2018 [Paper]
• Comparison-based inverse classification for interpretability in machine learning, IPMU 2018 [Paper]
• Quantitative program slicing: Separating statements by relevance, ICSE 2013 [Paper]
• Stratal slicing, Part II: Real 3-D seismic data, Geophysics 1998 [Paper]

Out-of-distribution Evaluation

• A brief review of domain adaptation, Transactions on Computational Science and Computational Intelligenc 2021 [Paper]
• Domain adaptation for medical image analysis: a survey, IEEE Transactions on Biomedical Engineering 2021 [Paper]
• Retiring adult: New datasets for fair machine learning, NeurIPS 2021 [Paper] [Code]
• Wilds: A benchmark of in-the-wild distribution shifts, ICML 2021 [Paper] [Code]
• Do image classifiers generalize across time?, ICCV 2021 [Paper]
• Using videos to evaluate image model robustness, arXiv 2019 [Paper]
• Regularized learning for domain adaptation under label shifts, ICLR 2019 [Paper] [Code]
• Benchmarking neural network robustness to common corruptions and perturbations, ICLR 2019 [Paper] [Code]
• Towards deep learning models resistant to adversarial attacks, ICLR 2018 [Paper] [Code]
• Robust physical-world attacks on deep learning visual classification, CVPR 2018 [Paper]
• Detecting and correcting for label shift with black box predictors, ICML 2018 [Paper]
• Poison frogs! targeted clean-label poisoning attacks on neural networks, NeurIPS 2018 [Paper] [Code]
• Practical black-box attacks against machine learning, CCS 2017 [Paper]
• Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models, AISec 2017 [Paper] [Code]
• Deepfool: a simple and accurate method to fool deep neural networks, CVPR 2016 [Paper] [Code]
• Evasion attacks against machine learning at test time, ECML PKDD 2013 [Paper] [Code]
• Adapting visual category models to new domains, ECCV 2010 [Paper]
• Covariate shift by kernel mean matching, MIT Press 2009 [Paper]
• Covariate shift adaptation by importance weighted cross validation, JMLR 2007 [Paper]

Prompt Engineering

• SPeC: A Soft Prompt-Based Calibration on Mitigating Performance Variability in Clinical Notes Summarization, arXiv 2023 [Paper]
• Making Pre-trained Language Models Better Few-shot Learners, arXiv 2021 [Paper] [Code]
• Bartscore: Evaluating generated text as text generation, NeurIPS 2021 [Paper] [Code]
• BERTese: Learning to Speak to BERT, arXiv 2021 [Paper]
• Few-shot text generation with pattern-exploiting training, arXiv 2020 [Paper]
• Exploiting cloze questions for few shot text classification and natural language inference, arXiv 2020 [Paper] [Code]
• It's not just size that matters: Small language models are also few-shot learners, arXiv 2020 [Paper]
• How can we know what language models know?, TACL 2020 [Paper] [Code]
• Universal adversarial triggers for attacking and analyzing NLP, EMNLP 2019 [Paper] [Code]

Data Maintenance

Data Understanding

• The science of visual data communication: What works, Psychological Science in the Public Interest 2021 [Paper]
• Towards out-of-distribution generalization: A survey, arXiv 2021 [Paper]
• Snowy: Recommending utterances for conversational visual analysis, UIST 2021 [Paper]
• A distributional framework for data valuation, ICML 2020 [Paper]
• A comparison of radial and linear charts for visualizing daily patterns, TVCG 2020 [Paper]
• A marketplace for data: An algorithmic solution, EC 2019 [Paper]
• Data shapley: Equitable valuation of data for machine learning, PMLR 2019 [Paper] [Code]
• Deepeye: Towards automatic data visualization, ICDE 2018 [Paper] [Code]
• Voyager: Exploratory analysis via faceted browsing of visualization recommendations, TVCG 2016 [Paper]
• A survey of clustering algorithms for big data: Taxonomy and empirical analysis, TETC 2014 [Paper]
• On the benefits and drawbacks of radial diagrams, Handbook of Human Centric Visualization 2013 [Paper]
• What makes a visualization memorable?, TVCG 2013 [Paper]
• Toward a taxonomy of visuals in science communication, Technical Communication 2011 [Paper]

Data Quality Assurance

• Human-AI Collaboration for Improving the Identification of Cars for Autonomous Driving, CIKM Workshop 2022 [Paper]
• A Human-ML Collaboration Framework for Improving Video Content Reviews, arXiv 2022 [Paper]
• Knowledge graph quality management: a comprehensive survey, TKDE 2022 [Paper]
• A crowdsourcing open platform for literature curation in UniProt, PLoS Biol. 2021 [Paper] [Code]
• Building data curation processes with crowd intelligence, Advanced Information Systems Engineering 2020 [Paper]
• Data Curation with Deep Learning, EDBT, 2020 [Paper]
• Automating large-scale data quality verification, VLDB 2018 [Paper]
• Data quality: The role of empiricism, SIGMOD 2017 [Paper]
• Tfx: A tensorflow-based production-scale machine learning platform, KDD 2017 [Paper] [Code]
• Discovering denial constraints, VLDB 2013 [Paper] [Code]
• Methodologies for data quality assessment and improvement, ACM Computing Surveys 2009 [Paper]
• Conditional functional dependencies for data cleaning, ICDE 2007 [Paper]
• Data quality assessment, Communications of the ACM 2002 [Paper]

Data Storage and Retrieval

• Dbmind: A self-driving platform in opengauss, PVLDB 2021 [Paper]
• Online index selection using deep reinforcement learning for a cluster database, ICDEW 2020 [Paper]
• Bridging the semantic gap with SQL query logs in natural language interfaces to databases, ICDE 2019 [Paper]
• An end-to-end learning-based cost estimator, VLDB 2019 [Paper] [Code]
• An adaptive approach for index tuning with learning classifier systems on hybrid storage environments, Hybrid Artificial Intelligent Systems 2018 [Paper]
• Automatic database management system tuning through large-scale machine learning, SIGMOD 2017 [Paper]
• Learning to rewrite queries, CIKM 2016 [Paper]
• DBridge: A program rewrite tool for set-oriented query execution, IEEE ICDE 2011 [Paper]
• Starfish: A Self-tuning System for Big Data Analytics, CIDR 2011 [Paper] [Code]
• DB2 advisor: An optimizer smart enough to recommend its own indexes, ICDE 2000 [Paper]
• An efficient, cost-driven index selection tool for Microsoft SQL server, VLDB 1997 [Paper]

Data Benchmark

Training Data Development Benchmark

• OpenGSL: A Comprehensive Benchmark for Graph Structure Learning, arXiv 2023 [Paper] [Code]
• REIN: A Comprehensive Benchmark Framework for Data Cleaning Methods in ML Pipelines, EDBT 2023 [Paper] [Code]
• Usb: A unified semi-supervised learning benchmark for classification, NeurIPS 2022 [Paper] [Code]
• A feature extraction & selection benchmark for structural health monitoring, Structural Health Monitoring 2022 [Paper]
• Data augmentation for deep graph learning: A survey, KDD 2022 [Paper]
• Blood-based transcriptomic signature panel identification for cancer diagnosis: benchmarking of feature extraction methods, Briefings in Bioinformatics 2022 [Paper] [Code]
• Amlb: an automl benchmark, arXiv 2022 [Paper]
• A benchmark for data imputation methods, Front. Big Data 2021 [Paper] [Code]
• Benchmark and survey of automated machine learning frameworks, JAIR 2021 [Paper]
• Benchmarking differentially private synthetic data generation algorithms, arXiv 2021 [Paper]
• A comprehensive benchmark framework for active learning methods in entity matching, SIGMOD 2020 [Paper]
• Rethinking data augmentation for image super-resolution: A comprehensive analysis and a new strategy, CVPR 2020 [Paper] [Code]
• Comparison of instance selection and construction methods with various classifiers, Applied Sciences 2020 [Paper]
• An empirical survey of data augmentation for time series classification with neural networks, arXiv 2020 [Paper] [Code]
• Toward a quantitative survey of dimension reduction techniques, IEEE Transactions on Visualization and Computer Graphics 2019 [Paper] [Code]
• Cleanml: A benchmark for joint data cleaning and machine learning experiments and analysis, arXiv 2019 [Paper] [Code]
• Comparison of different image data augmentation approaches, Journal of Big Data 2019 [Paper] [Code]
• A benchmark and comparison of active learning for logistic regression, Pattern Recognition 2018 [Paper]
• A publicly available benchmark for biomedical dataset retrieval: the reference standard for the 2016 bioCADDIE dataset retrieval challenge, Database (Oxford). 2017 [Paper] [Data]
• RODI: A benchmark for automatic mapping generation in relational-to-ontology data integration, ESWC 2015 [Paper] [Code]
• TPC-DI: the first industry benchmark for data integration, PVLDB 2014 [Paper]
• Comparison of instance selection algorithms II. Results and comments, ICAISC 2004 [Paper]

Inference Data Development Benchmark

• Beyond the imitation game: Quantifying and extrapolating the capabilities of language models, arXiv 2023 [Paper] [Code]
• Carla: a python library to benchmark algorithmic recourse and counterfactual explanation algorithms, arXiv 2021 [Paper] [Code]
• Benchmarking adversarial robustness on image classification, CVPR 2020 [Paper]
• Searching for a search method: Benchmarking search algorithms for generating nlp adversarial examples, ACL Workshop 2020 [Code]
• Benchmarking neural network robustness to common corruptions and perturbations, ICLR 2019 [Paper] [Code] [Code]

Data Maintenance Benchmark

• Chart-to-text: A large-scale benchmark for chart summarization, ACL 2022 [Paper] [Code]
• Scalability vs. utility: Do we have to sacrifice one for the other in data importance quantification?, CVPR 2021 [Paper] [Code]
• An evaluation-focused framework for visualization recommendation algorithms, IEEE Transactions on Visualization and Computer Graphics 2021 [Paper] [Code]
• Facilitating database tuning with hyper-parameter optimization: a comprehensive experimental evaluation, VLDB 2021 [Paper] [Code]
• Benchmarking Data Curation Systems, IEEE Data Eng. Bull. 2016 [Paper]
• Methodologies for data quality assessment and improvement, ACM Computing Surveys 2009 [Paper]
• Benchmark development for the evaluation of visualization for data mining, Information visualization in data mining and knowledge discovery 2001 [Paper]

Unified Benchmark

• Dataperf: Benchmarks for data-centric AI development, arXiv 2022 [Paper]