Heterogeneous multi-source data association analysis system and method

Abstract

The invention discloses a heterogeneous multi-source data association analysis system and method. The system comprises a heterogeneous multi-source data acquisition module, a heterogeneous multi-source data hybrid association module, a heterogeneous multi-source data semantic mapping module, a heterogeneous multi-source data fusion module and a heterogeneous multi-source data cleaning module. The heterogeneous multi-source data acquisition module is used for acquiring data information of different sources and transmitting the acquired data; the heterogeneous multi-source data hybrid association module provides a way for knowledge of the infrastructure and describes a space-time evolution process in a phenomenon occurrence process of the infrastructure; the heterogeneous multi-source data semantic mapping module establishes a relationship between low-level data and a corresponding label, so that conversion between bottom-level features and high-level semantics is realized; the heterogeneous multi-source data fusion module is used for judging a semantic distance between the data so as to perform fusion; and the heterogeneous multi-source data cleaning module is used for cleaning the data to ensure the validity and authenticity of the data.

Description

technical field [0001] The invention relates to the technical field of data analysis, in particular to a heterogeneous multi-source data association analysis system and method. Background technique [0002] At present, with the deepening of informatization and big data applications, more and more data sets are generated. In the scenario of massive and multi-data sets, the analysis of heterogeneous and multi-source data is particularly important, and in the process of data information query, the relevance and validity of data are important criteria for judging the data system, and in the data In the extraction of data, there are often errors such as incomplete data chains, data information on the data chains and overall attribute trends, etc., which bring inconvenience to the extraction and query of data information; [0003] At the same time, the ability to support heterogeneous information association analysis technology is insufficient. At present, information association...

AI Technical Summary

Problems solved by technology

In the scenario of massive and multi-data sets, the analysis of heterogeneous and multi-source data is particularly important, and in the process of data information query, the relevance and validity of data are important criteria for judging the data system, and in the data In the extraction of data, there are often errors such as incomplete data chains, data information on the data chains and overall attribute trends, etc., which bring inconvenience to the extraction and query of data information;

[0003] At the same time, the ability to support heterogeneous information association analysis technology is insufficient. At present, information association is mainly limited to the use of entity-relationship models of relational databases for data organization. There is a lack of research on deep mining and fuzzy information element association methods for heterogeneous multi-source data. Solve the complex cross-relationships between different types of source information and the association problems of the inherent essential links of information elements

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