Interactive query method suitable for multiple big data management systems

Abstract

The invention relates to an interactive query method suitable for multiple big data management systems. The method comprises the steps of 1) building an associated document model, which comprises a document set and an association set, wherein the association set is a set composed of associations among documents; 2) converting different original data models into associated document models, and connecting different data sources integrally through the associated document models; 3) based on the associated document models, establishing a unified query language suitable for multivariate data; and 4) by utilizing the unified query language suitable for the multivariate data, realizing unified query of a relational database, a graph database and a file system. According to the method, the unified query language suitable for the multivariate data management system is proposed for the first time, so that the unified query of the relational database, the graph database and the file system can be realized.

Description

technical field [0001] The invention relates to a query language, in particular to an interactive query language and a query method suitable for a big data management system, and belongs to the technical fields of big data and databases. Background technique [0002] With the continuous popularization of computers, the demand for data management and processing has become increasingly urgent. People have proposed different data models for different data forms and characteristics, and implemented corresponding data management systems to realize data management and analysis. Influential data models such as the E-R model have basically dominated the database world for more than 40 years since they were proposed in the 1970s. In the past ten years, with the deepening of Internet and Internet of Things applications, the generation of large-scale structured, semi-structured, and unstructured data has triggered the NoSQL movement [Cattell R. Scalable SQL and NoSQL data stores [J]. A...

AI Technical Summary

Problems solved by technology

Data-driven scientific discovery often requires scheduling a series of data pipelines to complete. It can be seen that these pipelines will span multiple processes such as data collection, batch writing, query, analysis, and visualization. There is a huge Question: How can pipeline programmers no longer consider the differences in the underlying data storage models, but can access and manipulate data in a unified way? This problem is mapped to data management technology, that is, how to cross the boundaries of SQL, NoSQL, and NewSQL databases, realize unified data access of multiple data models, and provide unified data operation interfaces for computing frameworks such as Hadoop and Spark

[0007] It is not difficult to see that there is still a lack of a unified query language for SQL and NoSQL databases, and graph databases are usually pushed to the other side of SQL query languages ​​due to their special query and analysis methods.

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