Unified Data Specification Language for Schema Customization
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Solution Overview
Problem
Existing data query languages require separate sets of statements for authoring and querying schemas, limiting flexibility and user customization, especially in creating Key Performance Indicators and supporting what-if analysis for Enterprise Performance Management applications.
Innovation Solution
The Data Specification Language (DaSL) allows users to enrich the schema by adding new attributes and dependencies using declarative expressions independent of the underlying data source, enabling the creation of personal objects that can be shared among users, and supports the definition of custom business objects and measures through a unified modeling language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate sets of statements are used for authoring and querying schemas, then the data query language maintains clear separation of concerns, but user flexibility and customization capability are limited
Solution Approach 1:
The patent combines schema authoring and schema querying capabilities into a single unified Data Specification Language. Users can both define new attributes/dependencies and query existing schemas using the same declarative language, eliminating the need for separate statement sets and thereby improving user flexibility while maintaining manageable language structure through consistent syntax rules.
Solution Approach 2:
The Data Specification Language serves multiple functions: it can define new attributes, establish dependencies between attributes, and query existing schemas all within the same language framework. This multi-functional approach allows a single language to handle both schema creation and data querying, enhancing versatility without significantly increasing complexity.
2Adaptability or versatility
If users can add new attributes and dependencies using declarative expressions, then customization capability for Key Performance Indicators is improved, but the complexity of the data model increases
Solution Approach 1:
The system provides predefined templates and standard attribute structures that users can extend. By establishing a solid foundation of pre-defined schemas and relationships, users can add custom attributes and dependencies without starting from scratch, which simplifies the customization process and reduces the perceived complexity even as customization capability increases.
Solution Approach 2:
Users can create new attributes and dependencies by copying and adapting existing schema patterns. The declarative language allows replication of proven schema structures with minimal modification, enabling rapid customization of Key Performance Indicators while maintaining consistency with the overall data model and avoiding unnecessary complexity.
3Ease of operation
If a unified language framework is used for defining custom business objects, then sharing and collaboration among users is improved, but the learning curve and initial complexity may increase
Solution Approach 1:
The unified Data Specification Language serves as a universal interface for all users to define, share, and query business objects. The same language that defines complex schemas can also express simple queries, making the system accessible to users with varying expertise levels and facilitating collaboration through a common language without requiring separate toolsets.
Solution Approach 2:
The language uses consistent parameters and syntax patterns throughout, allowing users to leverage familiar constructs when defining custom business objects. By maintaining parameter consistency across different language functions, the system reduces the effective learning curve despite the unified framework's comprehensive capabilities, as users apply the same syntactic rules regardless of the specific operation.
Data Source
AI summary
A system includes reception of a logical schema associated with data stored in a data store, generation of an input schema based on the logical schema, reception of an expression having a type of the input schema and comprising a first expression element defining an object, compilation of the first expression element based on the input schema to generate an output schema, and merger of the output schema and the input schema to generate a second input schema.