Intelligent data statistics and subscription system and method based on OLTP

Abstract

The invention discloses an intelligent data statistics and subscription system and method based on OLTP, which relate to the technical field of data application. The system comprises a big data statistics and subscription system and a data warehouse storage system. The big data statistics and subscription system comprises an engine rule configuration module, a mail subscription configuration module, an intelligent mail subscription module and a data aggregation service engine. The data warehouse storage system comprises a basic data warehouse and an intelligent scheduling engine; the basic data warehouse is divided into a basic layer, a KN1 layer, a KN2 layer and a PL layer. Efficient and rapid data services are provided for enterprises, so that the business decision-making efficiency is improved; the system provides original data acquisition of dimension per hour, provides a data scheduling mechanism based on minute granularity, and provides mail result scheduling of self-defined dimension, so that the efficiency is greatly improved, and the period of an internal process is greatly simplified.

Description

technical field [0001] The invention belongs to the field of data application technology, in particular to an OLTP-based intelligent data statistics and subscription system and method. Background technique [0002] Due to the data analysis needs of the decoration industry and the company's internal innovation decision-making needs, it is necessary to conduct multi-dimensional aggregation and comparative analysis of the associated data, form data results, and notify relevant technical decision makers. Traditional technical solutions mainly rely on manual processing, which is time-consuming and labor-intensive. If the amount of data exceeds a certain order of magnitude, it is difficult to process manually, and it is prone to data confusion; under the general trend of digital transformation in the decoration industry, the marketing decision-making department The demand for data is getting higher and higher, and there are more time-sensitive requirements, and such core demands c...

AI Technical Summary

Problems solved by technology

Traditional technical solutions mainly rely on manual processing, which is time-consuming and labor-intensive. If the amount of data exceeds a certain order of magnitude, it is difficult to process manually, and it is prone to data confusion; under the general trend of digital transformation in the decoration industry, the marketing decision-making department The demand for data is getting higher and higher, and there are more time-sensitive requirements, which cannot meet such core demands through manual methods

[0003] The existing technology has the following disadvantages: for the statistical analysis of large amounts of data, such as traffic data exceeding tens of millions, traditional manual methods cannot support it, and code scripts need to be written to achieve it, and the reuse rate is very low; for multi-dimensional system comparison For analysis, due to the high complexity, it is difficult to carry out associated statistics manually through Excel; many basic OLTP data are incrementally generated in real time, and traditional manual processing cannot guarantee timeliness; the subscription and sending of result sets are mainly done manually Mainly send emails, because the data dimensions and analysis requirements are different, and there are many types, which increases additional operating costs

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