Big data analysis and optimization and platform components

Communication operators are paying more and more attention to the value of data and are demanding more and bigger data technologies. Many companies have started to take advantage of their resources to tap the value of data and develop their own core business. The use of a high-performance, secure, scalable and easy-to-manage big data management system will help companies avoid the tedious system operation and maintenance, especially in the communication business system, and help them focus on their own business development. In this paper, we first investigate existing data management systems and analyze their strengths and weaknesses. In response to the problems of not too light, insufficient timeliness of data migration, and not enough innovation in data analysis, we design a more efficient, convenient and easy-to-use big data management platform. Firstly, the big data management system is designed. According to the process of big data processing, six modules of interface acquisition, program scheduling, data aggregation, platform alerting, marketing analysis and visualization are designed based on the communication big data platform architecture. The management system mainly focuses on data access and data mining analysis, so the main modules of this paper are program scheduling, data aggregation and marketing analysis modules, while other modules are based on the original big data management system of the enterprise with a small amount of improvement. In order to realize the needs of dynamically creating data testing environment and isolating production and experimental environments under the communication application scenario, a mechanism of the big data system for production and the virtualized system for experiments acting together is proposed. Then the corresponding scheduling module architecture process is designed and built, the corresponding scheduling rules and related scheduling information field tables are designed, and the data aggregation storage is improved. The program scheduling module was designed to be more lightweight and easy to use, and the data migration module increased the timeliness of data migration.(c) 2022 Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).