Service Investment and Pricing Strategies in E-commerce Platforms with Sellers Competition

With the development of the two-sided market, many platform enterprises classify their users into different types and cooperate with them with different strategies. The extant literature mainly explores the pricing and investment decisions for the platform, but pays little attention to the classification of sellers when making decisions. This paper investigates the investment of value-added service and pricing strategies for an e-commerce platform with competing sellers. Specifically, this paper considers a two-sided platform that is composed of an e-commerce platform, buyers and sellers. Sellers with high performance requirement and with low performance requirement compete for the buyers in the platform. This paper assumes that each buyer will choose the sellers’ type immediately after entering the platform and buy a unit of product in the platform. Through theoretical analysis the authors show that, the platform will gain more profits by investing in value-added services for type-A sellers and it will obtain the optimal profit when the transaction fee is moderate.

Multi-level Parallelization of Genotype Imputation on Supercomputers

Background: Genotype imputation as a service is developed to enable researchers to estimate genotypes on haplotyped data without performing whole genome sequencing. However, genotype imputation is computation intensive and thus it remains a challenge to satisfy the high performance requirement of genome wide association study (GWAS). Objective: In this paper, we propose a high performance computing solution for genotype imputation on supercomputers to enhance its execution performance. Method: We design and implement a multi-level parallelization that includes job level, process level and thread level parallelization, enabled by job scheduling management, message passing interface (MPI) and OpenMP, respectively. It involves job distribution, chunk partition and execution, parallelized iteration for imputation and data concatenation. Due to the design of multi-level parallelization, we can exploit the multi-machine/multi-core architecture to improve the performance of genotype imputation. Results: Experiment results show that our proposed method can outperform the Hadoop-based implementation of genotype imputation. Moreover, we conduct the experiments on supercomputers to evaluate the performance of the proposed method. The evaluation shows that it can significantly shorten the execution time, thus improving the performance for genotype imputation. Conclusion: The proposed multi-level parallelization, when deployed as an imputation as a service, will facilitate bioinformatics researchers in Singapore to conduct genotype imputation and enhance the association study.