A skyline query determines the data points in a dataset that are not dominated by others. It is widely used for many applications which require multi-criteria decision-making. However, skyline query processing is considerably time-consuming for a high-dimensional large-scale dataset. This study consists of two main tasks. The first is to design an efficient parallel computing technique to address the computational problem of skyline queries for large high-dimensional datasets. It is based on MapReduce frameworks to process large datasets. The second study focuses on the recommendation of upgrading products based on the skyline data points.A large number of efficient MapReduce skyline algorithms have been proposed in the literature. However, there are still opportunities for further parallelism. Our method to divide a large dataset is called by LShape partitioning strategy and we propose an effective filtering algorithm named propagation filtering. We verify that our algorithms outperformed the state-of-the-art approaches by extensive experiments, especially for high-dimensional large-scale datasets.The manufacturer often needs to make a proper decision to gain maximal profits from the product upgrading. The goal of upgrading products is to maximize the profit by increasing the total number of expected customers with a certain upgrading cost. Our algorithms in this study are based on the dominating relationships among products. It has been proved that finding the dominating relationship of products with three or more features is NP-hard. We first propose an optimal algorithm for upgrading a single product and then modify it to be an efficient heuristic algorithm with a percentage error approaching 20%. We also extend this heuristic algorithm for simultaneously upgrading multiple products. A skyline query determines the data points in a dataset that are not dominated by others. It is widely used for many applications which require multi-criteria decision-making. However, skyline query processing is considerably time-consuming for a high-dimensional large-scale dataset. This study consists of two main tasks. The first is to design an efficient parallel computing technique to address the computational problem of skyline queries for large high-dimensional datasets. It is based on MapReduce frameworks to process large datasets. The second study focuses on the recommendation of upgrading products based on the skyline data points.A large number of efficient MapReduce skyline algorithms have been proposed in the literature. However, there are still opportunities for further parallelism. Our method to divide a large dataset is called by LShape partitioning strategy and we propose an effective filtering algorithm named propagation filtering. We verify that our algorithms outperformed the state-of-the-art approaches by extensive experiments, especially for high-dimensional large-scale datasets.The manufacturer often needs to make a proper decision to gain maximal profits from the product upgrading. The goal of upgrading products is to maximize the profit by increasing the total number of expected customers with a certain upgrading cost. Our algorithms in this study are based on the dominating relationships among products. It has been proved that finding the dominating relationship of products with three or more features is NP-hard. We first propose an optimal algorithm for upgrading a single product and then modify it to be an efficient heuristic algorithm with a percentage error approaching 20%. We also extend this heuristic algorithm for simultaneously upgrading multiple products.
