In-Memory Database Information Model Generation via Semantic Layer Import
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Solution Overview
Problem
Users of semantic-based information design tools face the manual re-creation of data foundations for in-memory computing, which is time-consuming and inefficient, as they need to recreate entire tables and relationships from existing semantic layer data foundations.
Innovation Solution
A method for generating information models in an in-memory database system by importing data foundations from existing semantic layer files, where table objects and relationships are automatically extracted and populated onto a modeler canvas, allowing for the automatic identification and definition of cardinality between table columns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually re-create data foundation from existing semantic layer files, then information models can be created in in-memory database system, but the process is time-consuming and inefficient
Solution Approach 1:
The patent automatically copies and imports data foundation objects (tables, columns, relationships, cardinality) from existing semantic layer files into the in-memory database system. This eliminates manual re-creation by directly transferring the structural definitions and metadata from the source semantic layer to the target information model, significantly reducing time and effort.
Solution Approach 2:
The system performs preliminary actions by pre-defining the data foundation structure in semantic layer files before import. The semantic layer files contain pre-configured table definitions, column specifications, relationship mappings, and cardinality rules that are prepared in advance and then automatically applied during the import process to the in-memory database system.
2Ease of manufacture
If users manually recreate entire tables and relationships, then data foundation is established, but manual effort and complexity increase
Solution Approach 1:
The system enables self-service by automatically performing the data foundation creation process. The import functionality autonomously reads semantic layer files, extracts table and relationship definitions, and populates the in-memory database system without requiring manual intervention. This self-automating approach simplifies the process while reducing the perceived complexity for users.
Solution Approach 2:
The patent uses semantic layer files as an intermediary format that bridges the gap between existing data models and the in-memory database system. This intermediary contains all necessary structural information in a standardized format, allowing automatic translation and import, thereby reducing both manual effort and process complexity.
Data Source
AI summary
Various embodiments of systems and methods for generating information models in an in-memory database system by importing data foundation from existing Semantic layer files are described herein. The method includes specifying a type of information view to be generated to model content data. Further the method includes invoking the content data from existing semantic layer files using an import option of a content data editor interface. Subsequent to selecting one or more semantic layer files, automatically extracting table objects corresponding to the selected semantic layer files along with data foundation objects from a file source.