Big Data Market Optimization Pricing Model Based on Data Quality

Yang, Jiandong; Zhao, Chongchong; Xing, Chunxiao

doi:10.1155/2019/5964068

Cited by 25 publications

(22 citation statements)

References 34 publications

(34 reference statements)

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“…As shown in Tables 7 and 8, the marketability and utility of Table IMDJ are significantly higher in the subject's evaluation; while in Web IMDJ, these indicators are higher in the third-party-based evaluation, although there is no significant difference. The novelty of Web IMDJ is higher in the subject's evaluation, while the novelty of Table IMDJ Table 6 Total amount of the proposed knowledge Requirement Solution Additional DJ Table IMDJ 38 29 15 Web IMDJ 35 30 14 is higher in the third-party-based evaluation. Regarding the feasibility, the value is almost the same in both.…”

Section: Comparison Of the Number And Quality Of Proposed Solutionsmentioning

confidence: 96%

“…Liang et al [14] classified the existing data pricing models according to various aspects: economical [24,25], game theory [26,27], and auction [28,29] based. More recently, various types of improved data pricing and trading models have been proposed [30][31][32], showing the high expectation of data exchange. Nonetheless, as discussed in [14], the wide diversity of data prevents us from optimal trading.…”

Section: Situations and Issues Surrounding Data Utilizationmentioning

confidence: 99%

See 1 more Smart Citation

Development and Evaluation of a New Platform for Accelerating Cross-Domain Data Exchange and Cooperation

2019

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The technologies for collecting and analyzing data are developing significantly, enabling us to gain new knowledge from various types of data. Recently, the considerably increasing expectation for cross-domain data exchange and cooperation has attracted attention from data markets. However, as we lack data-utilization knowledge, an accurate evaluation of data is difficult; a non-price indicator for deciding the exchange of data is needed. Thus, in this study, we propose a new online platform for stakeholders to communicate regarding data utilization. Our online platform, called Web Innovators Marketplace on Data Jackets (Web IMDJ), is built with reference to the process of IMDJ workshops. Web IMDJ is superior to the conventional paper-based IMDJ (hereafter, Table IMDJ) in terms of reducing the burden on conducting workshops. Notably, Table IMDJ and Web IMDJ have different communication media, and this may affect the data-utilization knowledge proposed in the workshop. Therefore, we conducted workshops on both platforms under a controlled experimental environment to compare the proposed data-utilization knowledge. Consequently, the knowledge proposed in Web IMDJ gained equal or higher ratings by third-party evaluators (those who did not join in the experimental workshop). By contrast, subjects themselves evaluated the knowledge proposed in Table IMDJ as superior to Web IMDJ. These results revealed that both workshops have advantages as data-utilization platforms. Furthermore, we derived the best practices to utilize data effectively from a detailed analysis of the data obtained from the experimental workshops.

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Section: Comparison Of the Number And Quality Of Proposed Solutionsmentioning

confidence: 96%

Section: Situations and Issues Surrounding Data Utilizationmentioning

confidence: 99%

Development and Evaluation of a New Platform for Accelerating Cross-Domain Data Exchange and Cooperation

2019

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“…Improved gain and cost models: When we have multi-dimensional user contribution measures, the gain model can be much more complex. Similarly, the cost model may be more complex based on some specific pricing strategies [46,47]. Future work includes designing more complex gain-cost models and studying their effect on user selection.…”

Section: Conclusion and Future Workmentioning

confidence: 99%

Using Greedy Random Adaptive Procedure to Solve the User Selection Problem in Mobile Crowdsourcing

Yang

Ban

Xing

2019

Sensors

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With the rapid development of mobile networks and smart terminals, mobile crowdsourcing has aroused the interest of relevant scholars and industries. In this paper, we propose a new solution to the problem of user selection in mobile crowdsourcing system. The existing user selection schemes mainly include: (1) find a subset of users to maximize crowdsourcing quality under a given budget constraint; (2) find a subset of users to minimize cost while meeting minimum crowdsourcing quality requirement. However, these solutions have deficiencies in selecting users to maximize the quality of service of the task and minimize costs. Inspired by the marginalism principle in economics, we wish to select a new user only when the marginal gain of the newly joined user is higher than the cost of payment and the marginal cost associated with integration. We modeled the scheme as a marginalism problem of mobile crowdsourcing user selection (MCUS-marginalism). We rigorously prove the MCUS-marginalism problem to be NP-hard, and propose a greedy random adaptive procedure with annealing randomness (GRASP-AR) to achieve maximize the gain and minimize the cost of the task. The effectiveness and efficiency of our proposed approaches are clearly verified by a large scale of experimental evaluations on both real-world and synthetic data sets.

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“…In another study (14) , a big data market model is introduced and an optimal pricing scheme is formulated as a Stackelberg game to maximize the profit of the data source. Yet another study (15) , presents an optimal pricing scheme based on the quality of the provided data. However, studies (14) and (15) do not consider the privacy preferences of the data providers.…”

Section: Introductionmentioning

confidence: 99%

“…Yet another study (15) , presents an optimal pricing scheme based on the quality of the provided data. However, studies (14) and (15) do not consider the privacy preferences of the data providers.…”

Section: Introductionmentioning

confidence: 99%

Competitive equilibrium based personal data market model

Rekha¹,

Chatrapati²

2020

IJST

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Background/Objectives: In a digital economy, with the increasing commercial value, personal data is viewed as a commodity to be bought and sold. Data owners expect an appropriate compensation for trading off their privacy depending on how they value their privacy. Simultaneously, data consumers want to maximize their utility which is dependent on the value derived from the data. Consequently, a data market model that optimally recompenses data owners and maximizes the profits of data consumers is required. Methods: In this study, a data market model and pricing mechanism based on Fisher market model and competitive equilibrium are presented where the value derived from the data is calculated from information entropy. The proposed data model and the pricing mechanism jointly and simultaneously maximize the profit of data owners and the utility of data buyers. Findings: Experiments are conducted on adult data set to validate the efficacy of the proposed approach. Data owners are classified as risk averse, risk neutral, risk taking and privacy regarders. Subsequently, prices of data samples are adjusted to reach equilibrium as defined by the Fisher market model maximizing simultaneously and jointly the profit of data owners and the utility of data buyers. Applications: The proposed competitive equilibrium based personal data market model can be used to find equilibrium prices and bundles of data samples for each data buyer at these prices maximizing the utility of data buyer subject to his budgetary constraints and data requirements, and data owners' privacy preferences.

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Big Data Market Optimization Pricing Model Based on Data Quality

Cited by 25 publications

References 34 publications

Development and Evaluation of a New Platform for Accelerating Cross-Domain Data Exchange and Cooperation

Development and Evaluation of a New Platform for Accelerating Cross-Domain Data Exchange and Cooperation

Using Greedy Random Adaptive Procedure to Solve the User Selection Problem in Mobile Crowdsourcing

Competitive equilibrium based personal data market model

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