DIVINE: A pricing mechanism for outsourcing data classification service in data market
Abstract: Although data mining plays an essential role in enhancing business decision-making, currently, there are hardly any suitable online platforms available that can facilitate outsourcing data classification services in data markets. This paper presents an efficient and flexible online service platform architecture for data classification and delves into an in-depth study on pricing design to maximize revenue. To construct this online service platform, we address three significant challenges. Firstly, we focus on efficiently modeling accurate classifiers while minimizing consumption. Secondly, we design a flexible pricing mechanism with multiple levels. Lastly, we maximize online revenue based on incomplete information. Through comprehensive consideration of these three challenges, this paper proposes a query-based Data classIfication serVice with onlIne priciNg mEchanism, named DIVINE, which determines the transaction price of data classification services by learning buyers' valuations online. The theoretical analysis of this paper illustrates that DIVINE can ensure a constant (1+ϵ) competitive ratio compared to the maximal offline revenue with complete information. Finally, DIVINE is evaluated on UCI databases. The experimental results show that it not only exhibits low consumption, multiple levels, and two kinds of robustness but also outperforms existing state-of-the-art pricing mechanisms in terms of revenue, which can achieve about 90% of the optimal offline revenue and validates the theoretical analysis.
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