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.

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Cited By

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
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

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

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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]

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Publication History

Published: 06 December 2022

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

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Cited By

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
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

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