A Big Data Intelligent Aggregation Visualization and Control System

Abstract

The invention discloses an intelligent aggregation visualization and management control system for big data. The intelligent aggregation visualization and management control system comprises four functional groups, namely a data acquisition and cleaning group, a data classification and portraying group, a data aggregation and pushing group and a flow closed-loop and management control group. Compared with the prior art, the intelligent aggregation visualization and management control system for the big data automatically and intelligently meets continuously generated new digitalized service demands and support requirements in a search mode, and is high in expandability, high in maintainability, low in secondary development cost, high in practicality and easy to popularize.

Description

technical field [0001] The invention relates to the field of big data, in particular to a highly practical and intelligent aggregation visualization and control system of big data. Background technique [0002] The era of mobile Internet has arrived, and we are currently in an era of alternation of old and new, colorful, unknown is greater than known. For mobile Internet users, user personalization, differentiation, customization, and end-to-end service requirements are becoming more and more prominent; while for traditional operators, the replacement of traditional services such as voice and SMS by mobile Internet services is subversive. Therefore, traditional operators need to accelerate their transformation. In addition to their own network security and intelligent pipelines, they should also do a good job in end-to-end service support and digital services, improve customer perception, explore new ways, and find new growth points. [0003] The current mobile communicatio...

AI Technical Summary

Problems solved by technology

[0003] The current mobile communication network has data in B, O, and M domains. On the one hand, the data in the three domains is currently fragmented. Even in a specific domain, there are still chimney structures, data islands, and unintegrated data; on the other hand, it is difficult to support big data analysis and processing technologies. Real-time requirements; moreover, the presentation and analysis methods for data still remain in traditional reports and indicators, and even tens of thousands of reports have been implemented in some systems, with poor usability and long response time

In this case, the data is hidden in the specific system database, and the data consistency and accuracy are poor. In the traditional way, it is necessary to access the data of each system according to the specific application requirements and scenarios, generate analysis scenario data models, and develop reports and analysis functions. The development cycle is long, and new data islands are generated again, unable to respond to real-time personalized, differentiated, and customized analysis requirements, and unable to provide customer-oriented digital service requirements and end-to-end service support

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