DaSL Query Language for Complex Data Topology Retrieval

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Solution Overview

Problem

Existing query languages like SQL and MDX are limited in expressing complex data analysis and calculations, requiring multiple queries and additional processing to retrieve complex data topologies, which compromises atomicity and complicates data analysis tasks.

Innovation Solution

The introduction of Data Specification Language (DaSL) allows for defining a Data Schema and creating DaSL expressions using the same language, enabling the generation of semantic layer queries that bind to logical entities, facilitating the retrieval of complex data sets and supporting advanced data analysis without the need for multiple queries.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If SQL is used for data retrieval, then simplicity of query language is maintained, but expressiveness for complex data analysis is insufficient

Engineering Contradiction:

Improveexpressiveness for complex data analysisVSAvoidquery language complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary layer between the relational database and the user interface that translates high-level analytical queries into SQL statements. This intermediary handles complex data analysis operations while maintaining simplicity for end users, resolving the contradiction between query language simplicity and analytical expressiveness.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If multiple queries are issued to retrieve complex data topologies, then data retrieval capability is improved, but atomicity of read transaction is not guaranteed

Engineering Contradiction:

Improvedata retrieval capabilityVSAvoidatomicity of read transaction

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent merges multiple data retrieval operations into a single atomic query execution. The system constructs complex queries that can retrieve entire data topologies (including facts, dimensions, and hierarchies) in one database transaction, ensuring atomicity while maintaining enhanced data retrieval capability.

Inventive Principle:

Principle #5Merging (Combining)

3Adaptability or versatility

If Cube schema is used for MDX queries, then multi-dimensional analysis capability is improved, but design flexibility and adaptability to original schema are reduced

Engineering Contradiction:

Improvemulti-dimensional analysis capabilityVSAvoiddesign flexibility

Core Design Contradiction:

Adaptability or versatilityVSEase of manufacture

Solution Approach 1:

The patent implements a dynamic schema mapping system that automatically adapts the Cube model to the underlying relational schema. Rather than requiring static pre-defined Cubes, the system dynamically generates appropriate data models based on the original schema structure, maintaining both multi-dimensional analysis capability and design flexibility.

Inventive Principle:

Principle #15Dynamics

4Productivity

If client application reconciles data from multiple queries, then data processing capability is improved, but additional data processing overhead and complexity are introduced

Engineering Contradiction:

Improvedata processing capabilityVSAvoiddata processing complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent extracts the data reconciliation functionality from the client application and relocates it to the server-side query execution engine. The system automatically reconciles data from multiple sources and returns unified results, eliminating the need for client-side reconciliation logic and reducing both processing overhead and complexity.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS8572122B2Data schema and language

Publication Date: 2013.10.29 SAP SE

AI summary

A system includes reception of a logical schema associated with 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 representing the topology of a data set, the data set being an instance of the input schema, compilation of the expression to a data specification based on the input schema, and generation of a plan to execute on the data store based on the data specification.