Data query device and data query method based on semanteme

Abstract

The invention provides a data query device based on semantem. The device comprises: an acquiring unit, a semantic data association unit, an establishing unit, and a query unit, wherein the acquiring unit is used for acquiring the semantic metadata of a data source as semantic metadata for query; the semantic data association unit is used for generating an association associated with the semantic metadata according to configuration rules and searching the semantic metadata associated with the acquired semantic metadata based on a data structure of the association and the semantic metadata; the establishing unit is used for establishing a semantic association tree from the associations among multiple semantic metadata; and the query unit is used for selecting a query field from the semantic association tree established by the establishing unit and establishing a query expression according to a path relationship of the query field on the semantic association tree, so that the data can be queried. Accordingly, the invention provides a data query method based on the semanteme. Through the technical scheme, the difference of the underlaying data is shielded, the query modeling difficulty is reduced, the modeling efficiency is increased and the expandability is also guaranteed.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a semantic-based data query device and a semantic-based data query method. Background technique [0002] When the business personnel or decision makers of the enterprise conduct report query and analysis on the business information system, they need to build a query model based on the data of the business system on the premise of clarifying the analysis indicators, and then perform data retrieval for analysis. In the process of query model construction, the following difficulties and challenges are often encountered: [0003] 1. Since enterprise informatization is a long-term process, the data storage method and data structure are often different before and after. For example, for the same personnel information record table, the front and back may be stored in the format file and the relational database respectively; for the same name column, the front and back may be defined ...

AI Technical Summary

Problems solved by technology

Different data sources and data definitions, the inconsistency of these data entries has caused many difficulties in constructing query analysis

[0004] 2. Even if the data entry is unified, it is a challenge for most users when faced with highly technical concepts related to physical data structures and understanding the relationship between data structures and business entities

[0005] 3. The query analysis of each time is discrete on the whole, and it is difficult for different query analysis to provide each other with direct knowledge

The disadvantage of this implementation is that data capture for different data sources is complex in technical implementation, but for the business logic oriented to query modeling, there is no need to care about these

In addition, due to the direct query design for physical data, it is difficult to make it have the professional business background of the query designer, which will cause ambiguity and difficulty in use; and when creating a new query model, it is difficult to make existing queries It is difficult to achieve knowledge sharing between query models

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