Big data marketing method based on mobile signaling

Abstract

The invention discloses a big data marketing method based on mobile signaling. The big data marketing method is characterized by comprising the following steps: building an application type model base and an algorithm base, wherein the application type model base comprises application models of different application types; sampling input data, conducting principal component analysis (PCA) and keyword matching on the sampled data to determine the application type of the input data, and determine the corresponding application model of the input data according to the application type; carrying out data screening according to the application type of the input data; fragmenting the screened data, and conducting distributed saving on the fragmented data; determining that the application model corresponding to the application type of the input data does not exist in the application type model library according to the principal component analysis result; and conducting corresponding data transformation on the input data according to the format requirement, on the input data, of an obtained combined sorting algorithm. The big data marketing method can provide an accurate processing model for big data, and is universal to different application scenarios.

Description

technical field [0001] The invention belongs to the field of the Internet, and more specifically relates to a big data marketing method based on mobile signaling. Background technique [0002] With the rapid development of information technology and the diversification of people's access to information, all walks of life in society have a large amount of information data. A large amount of raw data has accumulated in the data warehouse of the telecom operator and has not been utilized. During the operation of the operator, a large number of customers have lost and business revenue has declined. In addition to using the existing business support system, more can only see the results of these phenomena through relevant reports, and then take corresponding measures. There is no prior warning, so that corresponding strategic adjustments cannot be taken in time. In addition, the data processing speed and response time of the current business support system are relatively slow, s...

AI Technical Summary

Problems solved by technology

However, this method is to perform high-precision multi-dimensional counting Bloom filter processing on multi-dimensional attribute data sets, and store multi-dimensional attribute data sets of a certain scale or with corresponding characteristics in the high-precision multi-dimensional counting Bloom filter. Large data sets with multi-dimensional attributes need to meet the input format requirements of the Bloom filter; in addition, this method only provides a method of "purifying" data value, and does not form a big data processing framework system

Images