Fact Partitioned Data Repository for Multi-Source Integration

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Solution Overview

Problem

Conventional data warehousing techniques, such as the dimensional and normalized approaches, face challenges in maintaining data integrity, flexibility, and scalability, particularly when integrating data from diverse operational systems and adapting to changing business processes, leading to complex schema designs and data duplication issues.

Innovation Solution

A data source system agnostic fact partitioned information repository that categorizes data by type rather than subject area, using customizable dimensions and data source specific mappings to translate data from various systems into a common format, allowing for efficient data insertion, retrieval, and business process customization without schema redesign.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If data is organized by subject area in dimensional approach, then data retrieval speed is improved, but data warehouse complexity increases when integrating multiple source systems

Engineering Contradiction:

Improvedata retrieval speedVSAvoiddata warehouse complexity

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The patent segments the data warehouse into standardized dimensional structures (time, location, entity, product dimensions) that can be consistently applied across multiple source systems. Each source system's data is partitioned into identical dimensional categories, enabling uniform retrieval operations while isolating system-specific complexities in separate data extraction layers.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a universal dimensional framework that serves multiple source systems simultaneously. The same dimensional structure (facts, time dimensions, location dimensions, entity dimensions, product dimensions) handles data from diverse operational systems, eliminating the need for separate subject-area-specific structures for each source system.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If data warehouse structure is modified to align with new business processes, then adaptability to changing business processes is improved, but data integrity and existing data become obsolete

Engineering Contradiction:

Improveadaptability to changing business processesVSAvoiddata integrity

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent implements dynamic dimension tables that can be configured and modified without changing the underlying data warehouse structure. Dimensions such as entity types, product categories, and location hierarchies can be updated to reflect new business processes while existing historical data remains intact and queryable under the original dimensional framework.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent establishes a pre-defined standardized dimensional structure that anticipates future business process changes. By embedding flexible dimension configurations in advance, the system can accommodate new business requirements through dimension parameter adjustments rather than structural redesigns, preserving historical data integrity.

Inventive Principle:

Principle #10Preliminary action

3Quantity of substance

If multiple dimensions are added to describe multiple subject areas, then data comprehensiveness is improved, but schema complexity and data duplication increase

Engineering Contradiction:

Improvedata comprehensivenessVSAvoidschema complexity

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The patent merges dimension definitions across what would traditionally be separate subject areas into unified dimensional structures. For example, entity dimensions consolidate customer, supplier, and employee information; product dimensions unify product catalogs across different operational contexts. This eliminates redundant dimension definitions and reduces schema complexity while maintaining comprehensive data representation.

Inventive Principle:

Principle #5Merging (Combining)

4Reliability

If data is stored in source system specific structural context, then data source fidelity is maintained, but data integration and comparison across sources become difficult

Engineering Contradiction:

Improvedata source fidelityVSAvoiddata integration ease

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The patent introduces standardized dimensional structures as intermediary layers between source systems and analytical queries. Source system data is mapped to these intermediate dimensional frameworks (time, location, entity, product dimensions), which provide a common language for cross-system comparisons while preserving source-specific details through dimension attributes and foreign key relationships.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11372880B2Data source system agnostic fact category partitioned information repository and methods for the insertion and retrieval of data using the information repository

Publication Date: 2022.06.28 CRYSP INTELLIGENCE PTY LTD

AI summary

There is provided data source system agnostic fact partitioned data information repository system comprising: a data repository comprising: a plurality of fact partitions; a plurality of dimensions stored in relation to the fact partitions, the plurality of dimensions shared by each of the fact partitions; and a plurality of data source system specific data mappings; a data receiver for receiving data from the plurality of data source systems; and a data mapper for partitioning the data into the plurality of fact partitions using the plurality of data source system specific data mappings.