دانلود رایگان مقاله استراتژی قیمت گذاری اطلاعات بر اساس کیفیت داده

abstract

This paper presents a bi-level mathematical programming model for the data-pricing problem that considers both data quality and data versioning strategies. Data products and data-related services differ from information products or services in terms of quality assessment methods. For this problem, we consider two aspects of data quality: (1) its multidimensionality and (2) the interaction between the dimensions. We designed a multi-version data strategy and propose a data-pricing bi-level programming model based on the data quality to maximize the profit by the owner of the data platform and the utility to consumers. A genetic algorithm was used to solve the model. The numerical solutions for the data-pricing model indicate that the multi-version strategy achieves a better market segmentation and is more profitable and feasible when the multiple dimensions of data quality are considered. These results also provide managerial guidance on data provision and data pricing for platform owners

6. Conclusions and future works

The emergence of data markets (e.g., Factual, Infochimps, Xignite and the Windows Azure Data Marketplace) has facilitated data transactions between providers and consumers. However, the shortcomings in existing data-pricing mechanisms in such marketplaces and the characteristics of the data products highlight the need for a fair and transparent pricing scheme. We here proposed a new pricing scheme for the data marketplace, aimed at providing a useful decision tool for both data-platform owners and data consumers. This scheme focuses on the value of data itself and considers the multiple attributes of the data. We specifically also consider the interactions between these attributes, which have so far been ignored. Furthermore, we analyzed the effect of a nonlinear utility function on production decision