Query-based pricing enables personalized data acquisition for data buyers, exhibiting potential in data markets. The state-of-the-art SQL query pricing strategy tackles the #P-hard arbitrage-free pricing task with the quadratic computational complexity, far from promptly fulfilling customer demands. In this paper, we propose a novel arbitrage-free and scalable pricing framework ARIA to calculate the prices for various query types in linear time, including select-project-join and simple aggregate (SPJA) queries. For the first time, we model what the query answer tells about the value of each tuple and formulate the tuple-level information of selection, projection, and simple aggregation queries. We develop several price functions based on the total information gain of all tuples. The containing relationship between the query information prevents possible arbitrage arising from query determinacy. We present efficient price computation algorithms to derive the prices of different types of queries with linear time complexity, which scan the common possible value set of tuples one time. In ARIA, the join query is decomposed as multiple single-relation queries for pricing in linear time. Extensive experiments on real and synthetic datasets demonstrate that, ARIA performs 3x faster than the state of the arts while enjoying desirable pricing characteristics.

Online data marketplaces emerge in diverse data-driven applications, where dynamically arriving consumers pur-chase the data at posted prices. The data value decays over time in many tasks, such as machine learning predictions and realtime systems. Existing query pricing methods do not consider the time-discounting data value. In this paper, we study the query feature-based data pricing problem with unknown time-discounting data valuation. We propose an effective online data pricing mechanism Pride to maximize the cumulative sales revenue. It leverages the powerful property of the ellipsoid method to efficiently solve online optimization via exploration and exploitation. Based on Thompson sampling, we present a novel non-stationary MAB algorithm Biased-TS to determine a suitable discount factor and attain the dynamic posted price. It is theoretically proved that, the regret upper bound order of Pride is dominated by the discretization error$O(\frac{T}{k})$, where$K$and$T$are the numbers of discount candidates and total trading rounds, respectively. Biased-TS gets a sub-linear regret upper bound$O(K^{3}\sqrt{T\ln T}+K\exp\{4\sqrt{\ln T}\})$. Extensive experiments using both synthetic and real datasets demonstrate that Pride yields around 90% of the optimal cumulative revenue, and it substantially outperforms the state-of-the-art methods.

Trading the machine learning-based prediction services has been up-and-coming for individuals and small companies. It serves to directly provide the predictions, e.g., classifications, for consumers without domain knowledge. Existing prediction service pricing methods closely rely on the strong assumption of completely known information on service quality and consumers’ valuations. In this paper, we study the profit maximization problem of pricing prediction services under incomplete information for the first time. We propose a novel Service Market model, named SMELT, considering multiple types of customers with dEmand and quaLity-aware valuaTions. We first derive the theoretical optimal solution to maximize service profit with complete information. Then, we develop an effective framework PSPricer under the profit ratio guarantee to solve the profit maximization problem with incomplete information. It is capable of not only efficiently getting the sub-optimal service price with bounded revenue loss, but also effectively estimating the service quality function with the maximum likelihood estimation. Extensive experiments on real-life datasets demonstrate our theoretical findings and the effectiveness and efficiency of PSPricer, compared with the state-of-the-art approaches.

Crowdsourcing techniques have been extensively explored in the past decade, including task allocation, quality assessment, and so on. Most of professional crowdsourcing platforms adopt the fixed pricing scheme to offer a fixed price for crowd tasks. It is neither incentive for crowd workers to produce good performance, nor profitable for the requester to gain high utility with low budget. In this article, we study the problem of pricing crowdsourcing tasks with optional bonuses. We propose a dynamic pricing mechanism, named CrowdPricer for incentively delivering bonuses to the crowd workers of completing tasks, in addition to offering a base payment for completing a task. We leverage a deep time sequence model to learn the effect of bonuses on workers’ quality for crowd tasks. CrowdPricer makes decisions on whether to provide bonuses on workers, so as to maximize the requester’s utility in expectation. We present an efficient bonus delivery algorithm under the help of beam search technique, in order to efficiently solve the decision making problem. Extensive experiments using both a real crowdsourcing platform and simulations demonstrate that CrowdPricer yields the higher utility for the requester. It also obtains more correct crowd answers than the state-of-the-art pricing methods.

Welfare maximization (WM) aims to select a group of seed nodes to allocate different items for marketing, so that the whole welfare after diffusion over a social network is maximized. It has attracted much attention due to the practical applications such as viral marketing and online advertisements, where the economic incentives are incorporated into users' adoption behaviors. However, existing studies ignore the time impact on the diffusion and consider a single item type. In this paper, we propose an effective time-aware utility-driven independent cascade (TUIC) model, that incorporates the time-aware multi-item propagation, utility-driven item adoption, and mixed item relationships together. We identify and formulate the time-aware welfare maximization problem. We develop a general framework to address the problem for mixed competitive, complementary, and independent items. It derives item allocation with the$(1 -1/e-\epsilon)$approximate social welfare in special cases. Extensive experiments on several real-life social networks demonstrate the effectiveness of TUIC model and the efficiency of the proposed framework, compared to the state of the arts.

Data have significant economic or social value in many application fields including science, business, governance, etc. This naturally leads to the emergence of many data markets such as GBDEx and YoueData. As a result, the data trade through data markets has started to receive attentions from both industry and academia. During the data buying and selling, how to price the data is an indispensable problem. However, pricing incomplete data is more challenging, even though incomplete data exist pervasively in a vast lot of real-life scenarios. In this paper, we attempt to explore thepricing problem for queries over incomplete data. We propose a sophisticated pricing mechanism, termed as${\sf iDBPricer}$, which takes a series of essential factors into consideration, including thedata contribution/usage,data completeness, andquery quality. We present two novel price functions, namely, the usage, and completeness-aware price function (UCA pricefor short) and the quality, usage, and completeness-aware price function (QUCA pricefor short). Moreover, we develop efficient algorithms for deriving the query prices. Extensive experiments using both real and benchmark datasets demonstrate${\sf iDBPricer}$is of excellent performance in terms of effectiveness and scalability, compared with the state-of-the-art price functions.

As data markets have started to receive much attention from both industry and academia, how to price the tradable data is an indispensable problem. Pricing incomplete data is more practical and challenging, due to the pervasiveness of incomplete data. In this paper, we explore the pricing problem for queries over incomplete data. We propose a sophisticated pricing mechanism, termed as iDBPricer, which considers a series of essential factors, including the data contribution/usage, data completeness, and query quality. We present two novel price functions, namely, the usage and completeness-aware price function (UCA price for short) and the quality, usage, and completeness-aware price function (QUCA price for short). Moreover, we develop efficient algorithms for deriving the query prices. Extensive experiments using both real and benchmark datasets confirm the superiority of iDBPricer to the state-of-the-art price functions.