Associate Professor Huiju Wang, a researcher of the base, has published a collaborative paper titled "Pricing Method for Path Queries with Label Constraints" in Journal of Frontiers of Computer Science and Technology.

Abstract: With the deepening recognition of the value of data elements across society, establishing a reasonable data pricing mechanism has become a core issue in promoting the development of the data elements market. In recent years, path query technology has achieved remarkable results in optimizing industry resource allocation efficiency and enhancing decision-making efficiency, continuously generating significant economic benefits for society. However, current research on graph data pricing primarily focuses on issues such as social network pricing and statistical query pricing, with limited studies on path query pricing. Therefore, this paper conducts research on path query pricing in graph data and proposes a personalized path query pricing mechanism. Specifically, this pricing mechanism, based on providing consumers with optimal solutions, considers different types of consumers’ preferences for path length and price, and returns the final price. It also allows consumers to query multiple paths in a single request. Additionally, the paper analyzes potential arbitrage issues in path query pricing and designs corresponding arbitrage-free mechanisms for each type of scenario. Furthermore, it develops an exact query pricing algorithm and an approximate query pricing algorithm for different query conditions. To avoid the additional computational costs induced by graph updates, the paper investigates dynamic query pricing and proposes a new solution to prevent recalculating query prices from scratch, thereby reducing computational costs. Finally, experiments using real and synthetic data verify that the proposed algorithms can effectively price large-scale graph data based on pricing theory.

Keywords: Graph Data Pricing, Path Query, Personalization, Arbitrage-free, Dynamic Query Pricing

Link: https://kns.cnki.net/kcms2/article/abstract

Author 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.