Hybrid Meta Model for Adaptive Data Warehouse Schema Management

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Solution Overview

Problem

Traditional data warehouse systems are inflexible and lack the ability to capture and adapt to complex hierarchical relationships and semantic information, making them inadequate for adaptive business processes and real-time data analysis.

Innovation Solution

A hybrid meta model that combines relational and semantic net approaches to dynamically manage meta data, enabling the automatic generation of data schemas and OLAP cube definitions, and facilitating the integration of business process monitoring data with data warehouses.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If traditional relational models are used to store data warehouse schemas, then data storage and querying are straightforward, but complex hierarchical relationships and semantic information cannot be captured adequately

Engineering Contradiction:

Improvesemantic informationVSAvoidmodel complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent merges relational models and semantic nets into a hybrid meta model that combines the structured storage capabilities of relational databases with the hierarchical relationship representation of semantic nets. This allows both data warehouse schemas and complex semantic information to be captured in a unified structure, resolving the contradiction between information completeness and model simplicity.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent embeds semantic net structures within the relational data warehouse schema, allowing hierarchical relationships to be nested within the traditional table structure. This nesting approach enables complex semantic information to be stored without completely redesigning the underlying data storage mechanism.

Inventive Principle:

Principle #7Nested doll (Nesting)

2Adaptability or versatility

If data warehouse schemas are designed independently from business processes, then schema design is simpler and more standardized, but the system cannot adapt to changes in the business environment

Engineering Contradiction:

Improveadaptability to business changesVSAvoidschema design complexity

Core Design Contradiction:

Adaptability or versatilityVSEase of manufacture

Solution Approach 1:

The patent introduces dynamic meta data management that allows the data warehouse schema to automatically adapt to business process changes. The system can dynamically update meta data about relationships between data and business processes, enabling the schema to evolve with changing business requirements without manual redesign.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent implements feedback mechanisms where business process information flows back into the data warehouse schema design. This feedback loop enables continuous alignment between business processes and data structure, allowing automatic adjustments to maintain adaptability while reducing manual design complexity.

Inventive Principle:

Principle #23Feedback

3Loss of information

If detailed meta data about relationships between data and business processes is captured, then data analysis from multiple perspectives becomes possible, but the system becomes more complex and requires more programming and integration work

Engineering Contradiction:

Improvemeta data completenessVSAvoidintegration work

Core Design Contradiction:

Loss of informationVSEase of operation

Solution Approach 1:

The patent enables the system to automatically generate and maintain meta data about relationships between data and business processes. The hybrid meta model self-updates as business processes change, eliminating the need for manual programming and integration work while maintaining complete meta data information.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent uses parameter changes in the meta model to represent evolving business relationships. By dynamically updating meta data parameters rather than requiring structural reprogramming, the system maintains information completeness while simplifying operations and reducing integration workload.

Inventive Principle:

Principle #35Parameter changes

4Ease of operation

If hierarchical relationships are mapped to database tables manually, then data storage is straightforward, but querying and modifying hierarchical meta data becomes cumbersome and complex

Engineering Contradiction:

Improvequerying hierarchical dataVSAvoiddatabase operations complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The patent introduces a semantic net layer as an intermediary between the relational database tables and the hierarchical relationships. This intermediary structure simplifies querying by providing a unified interface for accessing hierarchical data without requiring complex database operations, while the underlying relational tables remain straightforward for storage.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS7475080B2Adaptive data warehouse meta model method

Publication Date: 2009.01.06 SAP SE

AI summary

A hybrid approach for capturing meta data about Business Processing Monitoring (BPM) artifacts is based on a combination of a relational meta data model and a semantic net. Meta data about metrics and situations and their dimensional context are first captured in the method. Then, relational meta data are used to describe a generic data schema for metrics, situations and their dimensional context. The meta data from semantic nets are used to extend the meta data definitions. Data from a data warehouse are searched and managed with the schema described and managed with the relational and semantic net meta data.