Associate Professor Huiju Wang, a researcher of the base, has published a collaborative paper titled "Novel Practical Query Pricing Algorithm Based on Labor Game Model" in Journal of Computer Research and Development.

Journal of Computer Research and Development is an academic journal jointly sponsored by the Institute of Computing Technology of the Chinese Academy of Sciences and the China Computer Federation, and is publicly distributed both at home and abroad. Its purpose is to report the highest-level academic papers and the latest scientific research achievements in the field of computer science in China, and it has become one of the most influential Chinese academic journals in the computer discipline.

Abstract:

With the promotion of data as a production factor, traditional query pricing methods face tremendous challenges in practical applications due to their overly strict premise assumptions, limited support for flexibility and dynamics, and inadequate consideration of key factors. To address these issues, we innovatively design a query pricing algorithm based on the labor game model. This algorithm models the participants in data transactions as labor unions and employers, and treats the data trading platform and data buyers as the labor union and employers, respectively. The data trading platform (labor union) is responsible for the fair and transparent calculation of the value of each traded dataset (wages), aiming to facilitate transactions as much as possible. Data buyers determine the purchase quantities of datasets based on their estimated value, personal needs, and budgets, thereby achieving a pricing strategy that balances the interests of all three parties. Experimental results demonstrate that compared with the popular Stackelberg game model, our algorithm better accommodates the interests of all parties and ensures greater fairness. Compared with traditional query-based data pricing methods, our pricing algorithm is more practical, offers greater flexibility and dynamics, and can dynamically adjust prices in response to changes in query results. The time complexity of our pricing algorithm is O(N), where N is the number of datasets related to the query, and it also guarantees no arbitrage.

Keywords：query pricing; labor game model; data pricing; data trading; fair pricing  

Link：https://dx.doi.org/10.7544/issn1000-1239.202330791

Teacher profile

Huiju Wang, Associate Professor, currently serves as the deputy director of the Department of Information at Zhongnan University of Economics and Law. He has been committed to the research and development of basic theories, algorithms, and cutting-edge technologies in the field of data science and knowledge engineering. His key research areas include big data, high-performance databases, data warehouses, artificial intelligence, and data pricing. He has published more than 20 high-quality academic papers in the field of big data. The highest citation count of a single SCI English paper is more than 400 times (ESI highly cited paper), and the highest citation count of a single Chinese paper on CNKI is more than 1000 times. He has also authored a monograph on big data independently. He has won the Outstanding Paper Award of Chinese Journal of Computers (only 3 papers are selected every five years, with a selection rate of less than 3‰), the Outstanding Doctoral Dissertation Award of Renmin University of China, and other honors.