Data mining based massive data query system

Abstract

The invention discloses a data mining based massive data query system. The system comprises a data acquisition module, a data processing module, a data storage management module, a data mining module, a data service module and a data monitoring module. The data acquisition module is used for acquiring data to a data processing center from various data sources; the data processing module is used for decoding and format conversion of preliminarily processed data acquired by the data acquisition module; the data storage management module is used for storage management of data processed by the data processing module; the data mining module comprises a data source integration submodule, an associated word storage submodule, a mining submodule, an output submodule and a feedback submodule; the data service module is used for realizing interaction between the data stored in the data storage management module and users in various ways to meet data access demands of the users. The data mining based massive data query system has advantages of increase of massive data query speed, simplification of query ways, reduction of query cost and improvement of convenience in use of massive data.

Description

technical field [0001] The invention relates to a query system, in particular to a massive data query system based on data mining technology. Background technique [0002] With the rapid development of computer and network technology, the growth of information volume far exceeds the growth of people's ability to obtain information. The orderly storage and utilization of massive information resources has attracted more and more attention from all over the world. In the real world, in the field of mobile e-commerce, there is often a demand for querying, displaying and browsing transactions of massive data. However, in the face of massive data, the first thing we need to consider is how to improve the operational performance and processing efficiency of massive data. At present, the main measures taken by people to process massive data include: selecting excellent database tools, optimizing query statements, increasing virtual memory, partitioning data, establishing extensive...

AI Technical Summary

Problems solved by technology

The above strategies to solve the efficiency of massive data processing often require huge capital investment, either increasing the cost of hardware, or commissioning the developer's own quality, all of which need to increase costs, and cannot fundamentally solve the problem of processing efficiency of massive data

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