Dynamic data encapsulation method based on data item analysis, client and server

Abstract

The embodiment of the invention provides a dynamic data encapsulation method based on data item analysis, a client and a server. The method is characterized by comprising the following steps of acquiring a data item and/or a logic model selected by a user based on a given data item record and/or a logic model directory, and generating a query data item set and a relational algebraic operation treeaccording to the data item and/or the logic model selected by the user so as to obtain a data query request of the user; generating the query logic LSQL according to the data query request, and sending the query logic LSQL to a server to obtain a data query result set, wherein the query data item set comprises the data items selected by a user and/or a logic model, the relation algebraic operation tree is used for describing the query logic among data items in the data item set. According to the embodiment of the invention, the response efficiency of the data query requirement is improved, the operation and maintenance cost is reduced, and meanwhile, the data query requirement of a user is better met.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of computer data encapsulation, and more specifically, relate to a dynamic data encapsulation method based on data item analysis, a client and a server. Background technique [0002] At present, many large enterprises such as communications, finance and other industries have already planned to build a big data platform and build a big data operation system within the enterprise, so as to integrate the data resources of various departments, subordinate organizations and business sectors within the enterprise, and serve the enterprise Business analysis and data visualization applications provide more comprehensive data services. In these enterprise-level big data operating systems, most of them still use the method of directly opening access to the big data platform to allow data applications to access the data resources of the big data platform. However, this method has great security r...

AI Technical Summary

Problems solved by technology

Due to the experience and ability limitations of OpenAPI developers, as well as the variety and continuous innovation of data applications, it is difficult for the OpenAPI provided by the data sharing platform to fully meet the data access requirements of data applications. Customized Data Access Requirements

[0010] Second, the response to data access customization requirements is slow and inflexible

If there are requirements changes or deviations in requirements understanding during this process, the business logic that has been developed and implemented needs to be modified, resulting in a longer waiting time

These factors often become one of the risks of out-of-control data application development progress

[0011] Third, it has high operation and maintenance costs

Existing technical solutions implement the business logic of data query and analysis in OpenAPI, so it is difficult for these OpenAPIs to be completely universal, and there are often customized OpenAPI requirements proposed by data application developers

Therefore, the managers of the big data operation system need to configure the OpenAPI development team. In addition to in-depth understanding of all data resources connected to the data sharing platform, team members also need to understand business requirements, develop and implement data query and analysis logic, and consume A lot of operation and maintenance costs

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