research-article

Try before you buy: a practical data purchasing algorithm for real-world data marketplaces

Authors:

Santiago Andrés Azcoitia,

Nikolaos LaoutarisAuthors Info & Claims

DE '22: Proceedings of the 1st International Workshop on Data Economy

Pages 27 - 33

https://doi.org/10.1145/3565011.3569054

Published: 06 December 2022 Publication History

Abstract

Data trading is becoming increasingly popular, as evident by the appearance of scores of data marketplaces (DMs) in the last few years satisfying the demand for third-party data. For buyers, however, deciding whether paying the requested price makes sense can only be done after having tested the data on their ML model. In this paper, we propose a method for optimizing data purchasing decisions. We show that if a marketplace provides to potential buyers a measure of the performance of their models on individual datasets, then they can select which of them to buy with an efficacy that approximates that of knowing the performance of each possible combination of datasets offered by the DM. We call the resulting algorithm Try Before You Buy (TBYB) and demonstrate over synthetic and real-world datasets how TBYB can lead to near optimal data purchasing with only O(N) instead of O(2^N) information and execution time.

References

[1]

Advaneo. 2022. Advaneo. Access to the world of data. https://www.advaneo-datamarketplace.de/. Last accessed: Sep'22.

[2]

A. Agarwal, M. Dahleh, and T. Sarkar. 2019. A Marketplace for Data: An Algorithmic Solution. In Proc. of ACM EC'19.

[3]

S. Andrés and N. Laoutaris. 2022. A Survey of Data Marketplaces and Their Business Models. ACM SIGMOD Record 51, 3 (2022).

[4]

S. Andrés Azcoitia, C. Iordanou, and N. Laoutaris. 2021. What Is the Price of Data? A Measurement Study of Commercial Data Marketplaces. (2021). arXiv:2111.04427

[5]

S. Andrés Azcoitia, M. Paraschiv, and N. Laoutaris. 2022. Computing the Relative Value of Spatio-Temporal Data in Data Marketplaces. SIGSPATIAL'22 (2022).

[6]

Battlefin. 2022. Better Your Investments Using Alternative Data. https://www.battlefin.com/. Last accessed: Sep'22.

[7]

Y. M. Brovman, M. Jacob, N. Srinivasan, S. Neola, D. Galron, R. Snyder, and P. Wang. 2016. Optimizing Similar Item Recommendations in a Semi-Structured Marketplace to Maximize Conversion. In Proc. of RecSys'16.

[8]

CARTO. 2022. Marketplace. https://marketplace.carto.com/me. Last accessed: Sep'22.

[9]

J. Castro, D. Gomez, and J. Tejada. 2009. Polynomial calculation of the Shapley value based on sampling. Computers and Operations Research 36 (05 2009).

[10]

R. Castro Fernandez. 2022. Protecting Data Markets from Strategic Buyers. In Proceedings of SIGMOD'22.

Digital Library

[11]

R. Castro Fernandez, P. Subramaniam, and M. J. Franklin. 2020. Data Market Platforms: Trading Data Assets to Solve Data Problems. Proc. VLDB Endow. 13, 12 (2020).

[12]

S. Chawla, S. Deep, P. Koutris, and Y. Teng. 2019. Revenue maximization for query pricing. Proc. VLDB Endow. 13 (09 2019).

[13]

L. Chen, P. Koutris, and A. Kumar. 2019. Towards Model-Based Pricing for Machine Learning in a Data Marketplace. In Proc. of SIGMOD'19. ACM.

[14]

M. Dahleh. 2018. Why the Data Marketplaces of the Future Will Sell Insights, Not Data.

[15]

EU. 2016. General Data Protection Regulation (GDPR).

[16]

GeoDB. 2022. A Decentralized Big Data Ecosystem That Rewards You For The Data You Generate. https://geodb.com/. Last accessed: Sep'22.

[17]

A. Ghorbani and J. Zou. 2019. Data Shapley: Equitable Valuation of Data for Machine Learning. (04 2019).

[18]

S&P Global. 2022. Marketplace. https://www.marketplace.spglobal.com/en/. Last accessed: Sep'22.

[19]

IOTA. 2022. IOTA data marketplace. https://data.iota.org/. Last accessed: Oct'22.

[20]

R. Jia, D. Dao, B. Wang, F. A. Hubis, N. Hynes, N. M. Gürel, B. Li, C. Zhang, D. Song, and C. J. Spanos. 2019. Towards Efficient Data Valuation Based on the Shapley Value (Proc. of ML Research, Vol. 89).

[21]

Kaggle. 2015 (accessed Sep'22). ECML/PKDD 15: Taxi Trajectory Prediction. https://www.kaggle.com/c/pkdd-15-predict-taxi-service-trajectory-i/data

[22]

N. Kourtellis, K. Katevas, and D. Perino. 2020. FLaaS: Federated Learning as a Service. In Proc. of Workshop on Distributed Machine Learning.

[23]

P. Koutris, P. Upadhyaya, M. Balazinska, B. Howe, and D. Suciu. 2015. Query-Based Data Pricing. J. ACM 62, 5 (2015).

[24]

J. Lanier. 2013. Who Owns the Future? SIMON and SCHUSTER.

[25]

D. Moor. 2019. Data Markets with Dynamic Arrival of Buyers and Sellers. In Proc. of NetEcon '19.

Digital Library

[26]

State of California. 2018. California Consumer Privacy Act (CCPA).

[27]

City of Chicago. 2019 (accessed Sep'22). Taxi Trips. https://data.cityofchicago.org/Transportation/Taxi-Trips/wrvz-psew

[28]

O. Ohrimenko, S. Tople, and S. Tschiatschek. 2019. Collaborative Machine Learning Markets with Data-Replication-Robust Payments. ArXiv (2019). arXiv:1911.09052

[29]

Otonomo. 2022. One-Stop Shop for Vehicle Data. https://otonomo.io/. Last accessed: Sep'22.

[30]

Hubert P. and Ricco G. 2018. Imperfect information in macroeconomics. Sciences Po publications (2018).

[31]

J. Pei. 2020. Data Pricing - From Economics to Data Science. In Proc. of SIGKDD'20. ACM.

[32]

E. Posner and G. Weyl. 2018. Radical Markets. Uprooting Capitalism and Democracy for a Just Society. Princeton Univ. Press.

[33]

Refinitiv. 2022. Data Catalog. https://www.refinitiv.com/en/financial-data. Last accessed: Sep'22.

[34]

B. Rozemberczki, L. Watson, P. Bayer, H. Yang, O. Kiss, S. Nilsson, and R. Sarkar. 2022. The Shapley Value in Machine Learning. arXiv:2202.05594

[35]

Amazon Web Services. 2022. AWS Marketplace. https://aws.amazon.com/marketplace. Last accessed: Sep'22.

[36]

Lloyd S. Shapley. 1952. A Value for n-Person Games. (1952). https://www.rand.org/pubs/papers/P0295.html

[37]

Y. Shen, B. Guo, Y. Shen, X. Duan, X. Dong, and H. Zhang. 2016. A pricing model for Big Personal Data. Tsinghua Science and Technology 21 (10 2016), 482--490.

[38]

Shutterstock. 2022. Shutterstock. https://www.shutterstock.com/. Last accessed: Oct'22.

[39]

Snowflake. 2022. Marketplace. https://www.snowflake.com/marketplace/. Last accessed: Sep'22.

[40]

Yan T. and Procaccia A. 2020. If You Like Shapley Then You'll Love the Core.

[41]

TAUS. 2022. Data Marketplace. https://datamarketplace.taus.net/. Last accessed: Oct'22.

[42]

J. Yang, C. Zhao, and C. Xing. 2019. Big Data Market Optimization Pricing Model Based on Data Quality. Complexity 2019 (04 2019).

Cited By

Qian MMusa ABiswas MGuo YLiao WYu W(2025)Survey of Artificial Intelligence Model MarketplaceFuture Internet10.3390/fi1701003517:1(35)Online publication date: 14-Jan-2025
https://doi.org/10.3390/fi17010035
Nerini FBajardi PPanisson A(2024)Value is in the Eye of the Beholder: A Framework for an Equitable Graph Data EvaluationProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658919(467-479)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658919
Xue YWu C(2024)Recurrent neural network for solving several novel sales models: Try-Before-You-BuyInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02479-8Online publication date: 6-Dec-2024
https://doi.org/10.1007/s13042-024-02479-8
Show More Cited By

Index Terms

Try before you buy: a practical data purchasing algorithm for real-world data marketplaces
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Data management systems
    1. Information integration
      1. Data exchange

Recommendations

Measuring the price of data in commercial data marketplaces
DE '22: Proceedings of the 1st International Workshop on Data Economy

A large number of Data Marketplaces (DMs) have appeared in the last few years to help owners monetise their data, and data buyers optimize their marketing campaigns, train their ML models, and facilitate other data-driven decision processes. In this ...
Towards Trustworthy and Independent Data Marketplaces
ICBCT '20: Proceedings of the 2020 2nd International Conference on Blockchain Technology

Data is the new oil. In the past years the awareness about benefits of data has increased. A growing number of sectors recognize the opportunities from data. On the one hand it is very difficult for many researchers and enterprises to obtain data and on ...
The significant role of metadata for data marketplaces
DCMI'19: Proceedings of the 2019 International Conference on Dublin Core and Metadata Applications

With the shift to a data-driven society, data trading takes on a completely new significance. In the future, data marketplaces will be equivalent to other electronic commerce platforms such as Amazon or eBay. Just like any other online marketplace a ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

DE '22: Proceedings of the 1st International Workshop on Data Economy

December 2022

70 pages

ISBN:9781450399234

DOI:10.1145/3565011

Conference Chairs:
Nikos Laoutaris
IMDEA
,
Marco Mellia
Politecnico di Torino

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGCOMM: ACM Special Interest Group on Data Communication

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 December 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Horizon 2020 Framework Programme

Conference

CoNEXT '22

Sponsor:

SIGCOMM

CoNEXT '22: The 18th International Conference on emerging Networking EXperiments and Technologies

December 9, 2022

Rome, Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
118
Total Downloads

Downloads (Last 12 months)58
Downloads (Last 6 weeks)4

Reflects downloads up to 16 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Qian MMusa ABiswas MGuo YLiao WYu W(2025)Survey of Artificial Intelligence Model MarketplaceFuture Internet10.3390/fi1701003517:1(35)Online publication date: 14-Jan-2025
https://doi.org/10.3390/fi17010035
Nerini FBajardi PPanisson A(2024)Value is in the Eye of the Beholder: A Framework for an Equitable Graph Data EvaluationProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658919(467-479)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658919
Xue YWu C(2024)Recurrent neural network for solving several novel sales models: Try-Before-You-BuyInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02479-8Online publication date: 6-Dec-2024
https://doi.org/10.1007/s13042-024-02479-8
Zhang WChen PYang JSu JSheng Q(2023)Data Product-Oriented Services for Data Ecosystem2023 IEEE International Conference on Web Services (ICWS)10.1109/ICWS60048.2023.00102(755-762)Online publication date: Jul-2023
https://doi.org/10.1109/ICWS60048.2023.00102

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents