An Online Pricing Mechanism for Mobile Crowdsensing Data Markets

Zheng, Zhenzhe; Peng, Yanqing; Wu, Fan; Tang, Shaojie; Chen, Guihai

doi:10.1145/3084041.3084044

Cited by 51 publications

(13 citation statements)

References 21 publications

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“…In [26], a 'double or nothing' incentivecompatible mechanism is proposed to ensure workers behave honesty based on their self-confidence; this protocol is provable to avoid spammers from the crowd, under the assumption that every worker wants to maximize their expected payment. In [40,39,41,42,44,43], Zheng et al, the authors leverage the tools of game theory and mechanism design to analyze the interaction of rational and selfish mobile users, then design efficient incentive mechanisms for four classical and representative applications in mobile Internet: dynamic spectrum redistribution, mobile crowdsensing, data marketplace, and cloud bandwidth management, to stimulate selfish mobile users to cooperate, achieving a win-win situation.…”

Section: Mechanism Design In Crowdsourcingmentioning

confidence: 99%

“…Liu et al [16] proposed a context-aware data quality estimation in an online manner. Zheng et al [43] presented the first architecture of the mobile crowd-sensed data market, and conduct an in-depth study of the design problem of online data pricing. A novel online query-based crowd-sensed data pricing mechanism was proposed to determine the trading price of crowd-sensed data.…”

Section: Mechanism Design In Crowdsourcingmentioning

confidence: 99%

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A Brief Survey of Data Pricing for Machine Learning

Tang¹,

Lv²,

Wu³

2020

8th International Conference on Signal, Image Processing and Pattern Recognition

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Big data and machine learning are poised to revolutionize the field of artificial intelligence and represent a step towards building an intelligent society. Big data is considered to be the key to unlocking the next great waves of growth in productivity, the value of data is realized through machine learning. In this survey, we begin with an introduction to the general field of data pricing and distributed machine learning then progress to the main streams of data pricing and mechanism design methods. Our survey will cover several current areas of research within the field of data pricing, including the incentive mechanism design for federated learning, reinforcement learning, auction, crowdsourcing, and blockchain, especially, focus on reward function for machine learning and payment scheme. In parallel, we highlight the pricing scheme in data transactions, focusing on data evaluation via distributed machine learning. To conclude, we discuss some research challenges and future directions of data pricing for machine learning.

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Section: Mechanism Design In Crowdsourcingmentioning

confidence: 99%

Section: Mechanism Design In Crowdsourcingmentioning

confidence: 99%

A Brief Survey of Data Pricing for Machine Learning

Tang¹,

Lv²,

Wu³

2020

8th International Conference on Signal, Image Processing and Pattern Recognition

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“…The valuations are obtained using different generative random processes, so as to observe the algorithmic behavior under different scenarios. These generative models are motivated by studies modeling valuations for digital goods in online platforms and their pricing [43,18,34,45,47,13,48].…”

Section: Experimental Evaluationmentioning

confidence: 99%

Revenue maximization for query pricing

et al. 2019

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Buying and selling of data online has increased substantially over the last few years. Several frameworks have already been proposed that study query pricing in theory and practice. The key guiding principle in these works is the notion of arbitrage-freeness where the broker can set different prices for different queries made to the dataset, but must ensure that the pricing function does not provide the buyers with opportunities for arbitrage. However, little is known about revenue maximization aspect of query pricing. In this paper, we study the problem faced by a broker selling access to data with the goal of maximizing her revenue. We show that this problem can be formulated as a revenue maximization problem with single-minded buyers and unlimited supply, for which several approximation algorithms are known. We perform an extensive empirical evaluation of the performance of several pricing algorithms for the query pricing problem on real-world instances. In addition to previously known approximation algorithms, we propose several new heuristics and analyze them both theoretically and experimentally. Our experiments show that algorithms with the best theoretical bounds are not necessarily the best empirically. We identify algorithms and heuristics that are both fast and also provide consistently good performance when valuations are drawn from a wide variety of distributions.

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“…With improvements in catalogs and portal sites available in the data markets, opportunities for users to obtain data from data holders and providers have increased. Therefore, a data market has been developed in which various stakeholders exchange data and information about the data across different fields [1,2]. In particular, the developments of the Internet of things and cloud computing, and the privilege of mobile, digital markets for data have emerged [3,4].…”

Section: Introductionmentioning

confidence: 99%

Matrix-Based Method for Inferring Elements in Data Attributes Using a Vector Space Model

Hayashi

Ohsawa

2019

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This article addresses the task of inferring elements in the attributes of data. Extracting data related to our interests is a challenging task. Although data on the web can be accessed through free text queries, it is difficult to obtain results that accurately correspond to user intentions because users might not express their objects of interest using exact terms (variables, outlines of data, etc.) found in the data. In other words, users do not always have sufficient knowledge of the data to formulate an effective query. Hence, we propose a method that enables the type, format, and variable elements to be inferred as attributes of data when a natural language summary of the data is provided as a free text query. To evaluate the proposed method, we used the Data Jacket’s datasets whose metadata is written in natural language. The experimental results indicate that our method outperforms those obtained from string matching and word embedding. Applications based on this study can support users who wish to retrieve or acquire new data.

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An Online Pricing Mechanism for Mobile Crowdsensing Data Markets

Cited by 51 publications

References 21 publications

A Brief Survey of Data Pricing for Machine Learning

A Brief Survey of Data Pricing for Machine Learning

Revenue maximization for query pricing

Matrix-Based Method for Inferring Elements in Data Attributes Using a Vector Space Model

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