Built on

Zheng, Z., Padmanabhan, B.: On active learning for data acquisition. In: 2002 IEEE International Conference on Data Mining, 2002. Proceedings., pp. 562–569 (2002). IEEE
2002
Earlier work this paper cites.
Settles, B.: Active learning literature survey (2009)
2009
Earlier work this paper cites.
Rajpurkar, P., Jia, R., Liang, P.: Know what you don’t know: Unanswerable questions for squad. arXiv preprint arXiv:1806.03822 (2018)
Original
2018
Earlier work this paper cites.
Darst, B.F., Malecki, K.C., Engelman, C.D.: Using recursive feature elimination in random forest to account for correlated variables in high dimensional data. BMC genetics 19
2018
Earlier work this paper cites.
Agarwal, A., Dahleh, M., Sarkar, T.: A marketplace for data: An algorithmic solution. In: Proceedings of the 2019 ACM Conference on Economics and Computation, pp. 701–726 (2019)
2019
Earlier work this paper cites.
Chen, L., Koutris, P., Kumar, A.: Towards model-based pricing for machine learning in a data marketplace. In: Proceedings of the 2019 International Conference on Management of Data, pp. 1535–1552 (2019)
2019
Earlier work this paper cites.
Ghorbani, A., Zou, J.: Data shapley: Equitable valuation of data for machine learning. In: International Conference on Machine Learning, pp. 2242–2251 (2019). PMLR
2019
Earlier work this paper cites.
Jia, R., Dao, D., Wang, B., Hubis, F.A., Hynes, N., Gürel, N.M., Li, B., Zhang, C., Song, D., Spanos, C.J.: Towards efficient data valuation based on the shapley value. In: The 22nd International Conference on Artificial Intelligence and Statistics, pp. 1167–1176 (2019). PMLR
2019
Earlier work this paper cites.
Jia, R., Dao, D., Wang, B., Hubis, F.A., Gurel, N.M., Li, B., Zhang, C., Spanos, C.J., Song, D.: Efficient task-specific data valuation for nearest neighbor algorithms. arXiv preprint arXiv:1908.08619 (2019)
Original
2019
Earlier work this paper cites.
Pei, J.: A survey on data pricing: from economics to data science. IEEE Transactions on knowledge and Data Engineering 34
2020
Earlier work this paper cites.
Fernandez, R.C., Subramaniam, P., Franklin, M.J.: Data market platforms: Trading data assets to solve data problems. arXiv preprint arXiv:2002.01047 (2020)
Original
2020
Earlier work this paper cites.
Ghorbani, A., Kim, M., Zou, J.: A distributional framework for data valuation. In: International Conference on Machine Learning, pp. 3535–3544 (2020). PMLR
2020
Earlier work this paper cites.
Li, Y., Yu, X., Koudas, N.: Data acquisition for improving machine learning models. arXiv preprint arXiv:2105.14107 (2021)
Original
2021
Earlier work this paper cites.
Liu, J., Lou, J., Liu, J., Xiong, L., Pei, J., Sun, J.: Dealer: an end-to-end model marketplace with differential privacy. Proceedings of the VLDB Endowment 14
2021
Earlier work this paper cites.

Similar

Kwon, Y., Rivas, M.A., Zou, J.: Efficient computation and analysis of distributional shapley values. In: International Conference on Artificial Intelligence and Statistics, pp. 793–801 (2021). PMLR
2021
Cited alongside, same era.
Kwon, Y., Zou, J.: Beta shapley: a unified and noise-reduced data valuation framework for machine learning. arXiv preprint arXiv:2110.14049 (2021)
Original
2021
Cited alongside, same era.
Mazumder, M., Banbury, C., Yao, X., Karlaš, B., Rojas, W.G., Diamos, S., Diamos, G., He, L., Parrish, A., Kirk, H.R., et al.: Dataperf: Benchmarks for data-centric ai development. arXiv preprint arXiv:2207.10062 (2022)
Original
2022
Cited alongside, same era.
Data as a service market analysis. https://www.businesswire.com/news/home/20220520005242/en/Global-Big-Data-as-a-Service-Market-2022-to-2027---Industry-Trends-Share-Size-Growth-Opportunity-and-Forecasts---ResearchAndMarkets.com (2022)
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Gradient Health. https://gradienthealth.io (2023)
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Kang, F., Just, H.A., Sahu, A.K., Jia, R.: Performance scaling via optimal transport: Enabling data selection from partially revealed sources. arXiv preprint arXiv:2307.02460 (2023)
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2023

Data Acquisition: A New Frontier in Data-centric AI

Chen, Lingjiao, Acun, Bilge, Ardalani, Newsha et al.

Understand

As Machine Learning (ML) systems continue to grow, the demand for relevant and comprehensive datasets becomes imperative.

There is limited study on the challenges of data acquisition due to ad-hoc processes and lack of consistent methodologies.
We first present an investigation of current data marketplaces, revealing lack of platforms offering detailed information about datasets, transparent pricing, standardized data formats.
With the objective of inciting participation from the data-centric AI community, we then introduce the DAM challenge, a benchmark to model the interaction between the data providers and acquirers.

Built on

Zheng, Z., Padmanabhan, B.: On active learning for data acquisition. In: 2002 IEEE International Conference on Data Mining, 2002. Proceedings., pp. 562–569 (2002). IEEE
2002
Earlier work this paper cites.
Settles, B.: Active learning literature survey (2009)
2009
Earlier work this paper cites.
Rajpurkar, P., Jia, R., Liang, P.: Know what you don’t know: Unanswerable questions for squad. arXiv preprint arXiv:1806.03822 (2018)
Original
2018
Earlier work this paper cites.
Darst, B.F., Malecki, K.C., Engelman, C.D.: Using recursive feature elimination in random forest to account for correlated variables in high dimensional data. BMC genetics 19
2018
Earlier work this paper cites.
Agarwal, A., Dahleh, M., Sarkar, T.: A marketplace for data: An algorithmic solution. In: Proceedings of the 2019 ACM Conference on Economics and Computation, pp. 701–726 (2019)
2019
Earlier work this paper cites.
Chen, L., Koutris, P., Kumar, A.: Towards model-based pricing for machine learning in a data marketplace. In: Proceedings of the 2019 International Conference on Management of Data, pp. 1535–1552 (2019)
2019
Earlier work this paper cites.
Ghorbani, A., Zou, J.: Data shapley: Equitable valuation of data for machine learning. In: International Conference on Machine Learning, pp. 2242–2251 (2019). PMLR
2019
Earlier work this paper cites.
Jia, R., Dao, D., Wang, B., Hubis, F.A., Hynes, N., Gürel, N.M., Li, B., Zhang, C., Song, D., Spanos, C.J.: Towards efficient data valuation based on the shapley value. In: The 22nd International Conference on Artificial Intelligence and Statistics, pp. 1167–1176 (2019). PMLR
2019
Earlier work this paper cites.
Jia, R., Dao, D., Wang, B., Hubis, F.A., Gurel, N.M., Li, B., Zhang, C., Spanos, C.J., Song, D.: Efficient task-specific data valuation for nearest neighbor algorithms. arXiv preprint arXiv:1908.08619 (2019)
Original
2019
Earlier work this paper cites.
Pei, J.: A survey on data pricing: from economics to data science. IEEE Transactions on knowledge and Data Engineering 34
2020
Earlier work this paper cites.
Fernandez, R.C., Subramaniam, P., Franklin, M.J.: Data market platforms: Trading data assets to solve data problems. arXiv preprint arXiv:2002.01047 (2020)
Original
2020
Earlier work this paper cites.
Ghorbani, A., Kim, M., Zou, J.: A distributional framework for data valuation. In: International Conference on Machine Learning, pp. 3535–3544 (2020). PMLR
2020
Earlier work this paper cites.
Li, Y., Yu, X., Koudas, N.: Data acquisition for improving machine learning models. arXiv preprint arXiv:2105.14107 (2021)
Original
2021
Earlier work this paper cites.
Liu, J., Lou, J., Liu, J., Xiong, L., Pei, J., Sun, J.: Dealer: an end-to-end model marketplace with differential privacy. Proceedings of the VLDB Endowment 14
2021
Earlier work this paper cites.

Similar

Kwon, Y., Rivas, M.A., Zou, J.: Efficient computation and analysis of distributional shapley values. In: International Conference on Artificial Intelligence and Statistics, pp. 793–801 (2021). PMLR
2021
Cited alongside, same era.
Kwon, Y., Zou, J.: Beta shapley: a unified and noise-reduced data valuation framework for machine learning. arXiv preprint arXiv:2110.14049 (2021)
Original
2021
Cited alongside, same era.
Mazumder, M., Banbury, C., Yao, X., Karlaš, B., Rojas, W.G., Diamos, S., Diamos, G., He, L., Parrish, A., Kirk, H.R., et al.: Dataperf: Benchmarks for data-centric ai development. arXiv preprint arXiv:2207.10062 (2022)
Original
2022
Cited alongside, same era.
Data as a service market analysis. https://www.businesswire.com/news/home/20220520005242/en/Global-Big-Data-as-a-Service-Market-2022-to-2027---Industry-Trends-Share-Size-Growth-Opportunity-and-Forecasts---ResearchAndMarkets.com (2022)
2022
Cited alongside, same era.

Then

Twitter API. https://developer.twitter.com/en/products/twitter-api (2023)
2023
Closest in time.
Gradient Health. https://gradienthealth.io (2023)
2023
Closest in time.
Narrative. https://www.narrative.io (2023)
2023
Closest in time.
Kang, F., Just, H.A., Sahu, A.K., Jia, R.: Performance scaling via optimal transport: Enabling data selection from partially revealed sources. arXiv preprint arXiv:2307.02460 (2023)
Original
2023
Closest in time.
MLCommons: Croissant. https://github.com/mlcommons/croissant (2023)
2023
Closest in time.
TAUS Data Marketplace. https://datamarketplace.taus.net (2023)
2023
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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Zhao, M., Agarwal, N., Basant, A., Gedik, B., Pan, S., Ozdal, M., Komuravelli, R., Pan, J., Bao, T., Lu, H., Narayanan, S., Langman, J., Wilfong, K., Rastogi, H., Wu, C.-J., Kozyrakis, C., Pol, P.: Understanding data storage and ingestion for large-scale deep recommendation model training: Industrial product. In: Proceedings of the 49th Annual International Symposium on Computer Architecture. ISCA ’22, pp. 1042–1057 (2022)

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Andres, S., Laoutaris, N., et al

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Chen, J., Li, M., Xu, H.: Selling data to a machine learner: Pricing via costly signaling. In: International Conference on Machine Learning, pp. 3336–3359 (2022). PMLR

2022

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Cong, Z., Luo, X., Pei, J., Zhu, F., Zhang, Y.: Data pricing in machine learning pipelines. Knowledge and Information Systems 64

2022

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Nasdaq Data Link. https://data.nasdaq.com (2023)

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Amazon AWS Data Exchange. https://aws.amazon.com/data-exchange (2023)

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Databricks Marketplace. https://marketplace.databricks.com (2023)

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Google Cloud Vision API. https://cloud.google.com/vision/docs/drag-and-drop (2023)

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PromptBase. https://promptbase.com/ (2023)

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Snowflake Datamarketplace. https://www.snowflake.com/en/data-cloud/marketplace/ (2023)

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Jiang, K.F., Liang, W., Zou, J., Kwon, Y.: Opendataval: a unified benchmark for data valuation (2023)

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