Research on Web Service Selection Based on Parallel Skyline Algorithm
Xinmei Liang, Luqin · 2019
With the continuous development of the Internet, there are many web services with the same functional attributes but different functional attributes. It is urgent to find a web service that can satisfy itself quickly and efficiently from the massive web service data. This paper improves the traditional Skyline algorithm, divides the web service data set into regions, greatly reduces the data points without dominance, and saves memory usage. The improved Skyline algorithm can significantly improve the speed of Web service selection. However, the improved Skyline algorithm will still have insufficient computing resources when processing massive Web service data, resulting in a significant decrease in computing speed and even computer jam. In view of the above situation, this paper will parallelize the improved Skyline algorithm and parallelize the improved Skyline algorithm through the Spark platform. Experiments show that the parallelized Skyline algorithm can better handle massive Web service data.