Revenue maximization for query pricing

Chawla, Shuchi; Deep, Shaleen; Koutrisw, Paraschos; Teng, Yifeng

doi:10.14778/3357377.3357378

Cited by 36 publications

(23 citation statements)

References 64 publications

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“…However, most queries considered by these marketplaces are too simplistic to support sophisticated data analytics and decision making. Recently, Chawla et al [28] investigated three types of succinct pricing functions and studied the corresponding revenue maximization problems. 3.…”

Section: A Data Pricingmentioning

confidence: 99%

Data Boundary and Data Pricing Based on the Shapley Value

Tian

Yu-rong

et al. 2022

IEEE Access

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Data pricing is to price data as asset to promote the healthy development of data share, exchange, and reuse. However, the uncertain value index and neglection of interactivity lead to information asymmetry in the transaction process. A perfect data pricing system and a well-designed data market can widely promote data transactions. We take a three-agent data market in this paper, the data owner who provides the data record, the model buyer who is interested in buying machine learning (ML) model instances, and the broker who interacts between the data owner and the model buyer. Two interaction scenarios are defined. In scenario 1, we introduce the Shapley value (SV) to measure values of data records fairly and construct a revenue optimization problem based on the sum of the SV for data boundary. The optimal solution is obtained by calculating their derivatives. In scenario 2, we utilize market research and construct a revenue maximization (RM) problem to price the ML models. Further,An Integer Linear Programming for the RM problem (RM-ILP) process is proposed to transform the RM problem into an equivalent integer linear programming (ILP) problem and solve it with the Gurobi solver. Finally, we conduct extensive experiments, which validate that the RM-ILP process can provide high revenue to the broker and high affordability to the model buyers compared to the benchmarks.

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Section: A Data Pricingmentioning

confidence: 99%

Data Boundary and Data Pricing Based on the Shapley Value

Tian

Yu-rong

et al. 2022

IEEE Access

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“…A recent survey [39] presents an overview of works related to data pricing from a data science perspective. Other works [11][12][13]32], present pricing-specific problems for queries and models. Pricing mechanisms for data are an orthogonal problem to our work.…”

Section: Related Workmentioning

confidence: 99%

Data acquisition for improving machine learning models

Koudas

2021

Proc. VLDB Endow.

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The vast advances in Machine Learning (ML) over the last ten years have been powered by the availability of suitably prepared data for training purposes. The future of ML-enabled enterprise hinges on data. As such, there is already a vibrant market offering data annotation services to tailor sophisticated ML models. In this paper, inspired by the recent vision of online data markets and associated market designs, we present research on the practical problem of obtaining data in order to improve the accuracy of ML models. We consider an environment in which consumers query for data to enhance the accuracy of their models and data providers who possess data make them available for training purposes. We first formalize this interaction process laying out the suitable framework and associated parameters for data exchange. We then propose two data acquisition strategies that consider a trade-off between exploration during which we obtain data to learn about the distribution of a provider's data and exploitation during which we optimize our data inquiries utilizing the gained knowledge. In the first strategy, Estimation and Allocation (EA), we utilize queries to estimate the utilities of various predicates while learning about the distribution of the provider's data; then we proceed to the allocation stage in which we utilize those learned utility estimates to inform our data acquisition decisions. The second algorithmic proposal, named Sequential Predicate Selection (SPS), utilizes a sampling strategy to explore the distribution of the provider's data, adaptively investing more resources to parts of the data space that are statistically more promising to improve overall model accuracy. We present a detailed experimental evaluation of our proposals utilizing a variety of ML models and associated real data sets exploring all applicable parameters of interest. Our results demonstrate the relative benefits of the proposed algorithms. Depending on the models trained and the associated learning tasks we identify trade-offs and highlight the relative benefits of each algorithm to further optimize model accuracy.

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“…Chawla et al [15] extend the pricing function in Equation 3 to maximize seller revenue. They consider the setting that the supply is unlimited and the buyers are single-minded, that is, a buyer only wants to buy a single query bundle Q.…”

Section: Pricing Data Queriesmentioning

confidence: 99%

Data Pricing in Machine Learning Pipelines

Cong¹,

Liu²,

Pei³

et al. 2021

Preprint

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Machine learning is disruptive. At the same time, machine learning can only succeed by collaboration among many parties in multiple steps naturally as pipelines in an eco-system, such as collecting data for possible machine learning applications, collaboratively training models by multiple parties and delivering machine learning services to end users. Data is critical and penetrating in the whole machine learning pipelines. As machine learning pipelines involve many parties and, in order to be successful, have to form a constructive and dynamic eco-system, marketplaces and data pricing are fundamental in connecting and facilitating those many parties. In this article, we survey the principles and the latest research development of data pricing in machine learning pipelines. We start with a brief review of data marketplaces and pricing desiderata. Then, we focus on pricing in three important steps in machine learning pipelines. To understand pricing in the step of training data collection, we review pricing raw data sets and data labels. We also investigate pricing in the step of collaborative training of machine learning models, and overview

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Revenue maximization for query pricing

Cited by 36 publications

References 64 publications

Data Boundary and Data Pricing Based on the Shapley Value

Data Boundary and Data Pricing Based on the Shapley Value

Data acquisition for improving machine learning models

Data Pricing in Machine Learning Pipelines

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