Distributed fission query method and device

Abstract

The invention relates to a distributed fission query method and device. The method comprises the following steps of establishing a fission word file and a fission model file in a preset distributed system development framework; respectively searching fission words in the fission word file and fission modes in the fission mode file according to first support information for a fission mode from a preset fission word and second support information for a term from a fission mode of the term; carrying out iterative fission search according to the fission word file and the fission mode file; determining a fission word set and a fission mode set; carrying out fission processing on a to-be-queried statement; and obtaining a query result according to the processing result. According to the method and device provided by the invention, fission files are established in the distributed system development framework, and fission sets are determined to carry out fission processing on to-be-queried statements input by users and then return query results, so that the data mining time is shortened, the query correctness and query efficiency are improved, and the user experience is enhanced.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a distributed fission query method and device. Background technique [0002] The rapid development of network, communication and computer technology has also greatly promoted the progress of artificial intelligence technology. With the increasing maturity of text sentiment analysis and natural language processing technology, it has become a major demand and trend in the Internet era to study big data through intelligent analysis of computer applications. In this context, speech processing and data mining have also received more and more attention. Speech processing can identify what the speaker is saying and convert it into text data, and then the continuously growing amount of data can be stored permanently to provide a basis for subsequent data mining and data query. [0003] At present, offline data mining before data query mostly uses single thread and single process to p...

AI Technical Summary

Problems solved by technology

[0003] At present, offline data mining before data query mostly uses single thread and single process to process data, coupled with the rapid growth rate of data, resulting in long data mining time, low mining efficiency, and even easy to cause system crashes, which in turn affects subsequent Accuracy of user query data

In addition, in the existing technology, keywords and patterns are manually formulated, and the data mining method of identifying targets through pattern matching has low scalability and is difficult to apply on a large scale; while formulating patterns based on keywords and sentence structures, and identifying objects by calculating similarity with patterns The target data mining method requires a large amount of labeled data, and its effect largely depends on the coverage of samples, and the initial investment is too large

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