Heterogeneous Data Management via Unified Layered Architecture

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Traditional software design approaches are inefficient and inflexible in managing heterogeneous data, requiring multiple datasets or large datasets to encompass all possible data types, which limits adaptability and expandability.

Innovation Solution

A heterogeneous data management methodology and system that uses a four-layer architecture, including a common data layer, data abstraction layer, intelligence layer, and user interface layer, driven by data-logic templates, allowing for dynamic and incremental addition of new asset or work order types without requiring complete database table overhauls, enabling efficient storage and processing of diverse data types in a single system.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multiple datasets are created for different data types, then data management becomes more organized, but system complexity increases

Engineering Contradiction:

Improvedata management flexibilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent merges multiple heterogeneous data types into a single unified dataset structure. Instead of creating separate datasets for different data types, the invention uses a common data structure with dynamic field mapping that can accommodate various data types (assets, work orders, inspections, etc.) in one unified system, reducing system complexity while maintaining adaptability

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent creates a universal dataset structure that serves multiple functions. The common data structure with configurable fields and data-logic templates can handle different data types and business logic through a single unified interface, eliminating the need for multiple specialized datasets and reducing overall system complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If a large dataset is used to encompass all possible data fields, then data coverage is improved, but storage efficiency decreases

Engineering Contradiction:

Improvedata coverageVSAvoidstorage efficiency

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The patent applies local quality by making data fields configurable and dynamic rather than static. Each record can have fields defined according to its specific data type and business logic requirements, allowing the system to store only the necessary data for each record type, improving storage efficiency while maintaining comprehensive data coverage

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent introduces dynamic field definitions and data-logic templates that allow the dataset structure to adapt to different data types and requirements. Fields can be added, removed, or modified based on specific business needs without restructuring the entire dataset, enabling efficient storage while maintaining versatility

Inventive Principle:

Principle #15Dynamics

3Device complexity

If traditional datasets are used for predefined data fields, then data structure simplicity is maintained, but adaptability to change decreases

Engineering Contradiction:

Improvedata structure simplicityVSAvoidflexibility to change

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The patent transforms static predefined data fields into dynamic configurable fields. The system uses data-logic templates and field definitions that can be modified at runtime to accommodate new data types and business requirements, maintaining simple data structure access while enabling high adaptability to change

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent segments the data structure into configurable components (fields, data-logic templates, data types) that can be independently defined and modified. This segmentation allows the system to maintain overall structural simplicity while enabling flexible adaptation through individual component configuration

Inventive Principle:

Principle #1Segmentation

4Adaptability or versatility

If new asset or work order types are added to the system, then system functionality is improved, but programming changes are required

Engineering Contradiction:

Improvesystem expandabilityVSAvoidprogramming effort

Core Design Contradiction:

Adaptability or versatilityVSEase of manufacture

Solution Approach 1:

The patent enables the system to define and accommodate new asset or work order types through self-service configuration rather than requiring programming changes. The data-logic templates and field definition mechanisms allow users to configure new data types and business logic through the existing framework, eliminating the need for custom programming while maintaining system expandability

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS10607187B2Heterogeneous data management methodology and system

Publication Date: 2020.03.31 TRAXID LLC

AI summary

A system for storing, interpreting, displaying, and processing heterogeneous data comprises a common data layer configured to manage and store abstracted data using a standard relational database, the common data layer comprises a template repository storing a plurality of data-logic templates and user data. The system further includes a data abstraction layer comprising rules for processing user data and handling a user-interface, an intelligence layer comprises context sensitive processing logic of user inputs and data from the data abstraction layer according to the data-logic templates, and a user interface layer configured to present the processed data and capture user inputs for the system.