Hierarchical Database Microservice Conversion via ML Clustering

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Converting monolithic database applications into microservices is challenging, especially for systems with multi-tiered, hierarchical data structures, as they are difficult to modify and scale to meet increasing user demands and system needs.

Innovation Solution

A computer-implemented method using Machine Learning algorithms, such as the K-Nearest Neighbor algorithm, to identify and cluster data elements associated with specific functions in hierarchical databases, allowing for the selection of relevant code elements and data elements to be converted into microservices, thereby decoupling services and improving system flexibility and reliability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a monolithic architecture is used to provide multiple data-driven functions, then system completeness and initial development are achieved, but system flexibility, scalability, and ease of modification deteriorate

Engineering Contradiction:

Improvesystem flexibilityVSAvoidmonolithic system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies segmentation by dividing the monolithic database system into multiple independent microservices, each handling specific data functions. The system identifies functional boundaries within the hierarchical database and creates separate service modules that can be independently deployed, modified, and scaled, directly resolving the contradiction between system flexibility and complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent extracts specific data functions from the monolithic system to create independent microservices. By identifying and separating key functional components that interact with the hierarchical database, the system enables individual services to be developed and maintained independently, improving adaptability while managing complexity.

Inventive Principle:

Principle #2Taking out (Extraction)

2Productivity

If a monolithic database system is designed to handle increasing user capacity, then initial user needs are met, but ongoing service capability and scalability deteriorate

Engineering Contradiction:

Improveuser capacityVSAvoidongoing service capability

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent implements dynamics by creating a modular microservice architecture where services can be dynamically added, removed, or scaled based on user capacity needs. Each microservice can be independently replicated and distributed across infrastructure, enabling the system to adapt to changing user loads without reconfiguring the entire monolithic system.

Inventive Principle:

Principle #15Dynamics

3Reliability

If microservices are used to improve system flexibility and reliability, then decoupling of independent services is achieved, but conversion difficulty from monolithic systems increases

Engineering Contradiction:

Improvesystem reliabilityVSAvoidconversion ease

Core Design Contradiction:

ReliabilityVSEase of manufacture

Solution Approach 1:

The patent applies preliminary action by providing automated tools and methodologies that analyze the existing monolithic system structure before conversion begins. The system pre-identifies functional boundaries, data dependencies, and service boundaries in the hierarchical database, preparing a conversion roadmap that reduces the difficulty of the actual microservices extraction and deployment process.

Inventive Principle:

Principle #10Preliminary action

4Adaptability or versatility

If hierarchical database structures are converted to microservices, then system modularity and flexibility improve, but identification and clustering of relevant data elements becomes more difficult

Engineering Contradiction:

Improvesystem modularityVSAvoiddata element identification difficulty

Core Design Contradiction:

Adaptability or versatilityVSDifficulty of detecting and measuring

Solution Approach 1:

The patent replaces manual analysis methods with automated machine learning algorithms that analyze hierarchical database structures, code elements, and activity logs. The ML system automatically identifies patterns, clusters related data elements, and determines service boundaries, substituting complex manual detection processes with computational algorithms that handle the hierarchical complexity efficiently.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11880387B2Cognitive analysis of hierarchical database elements for generation of microservices

Publication Date: 2024.01.23 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A computer identifies, within a hierarchical database, data elements associated with a selected function associated with the database, comprising. The computer identifies at least one function associated with a hierarchical database containing data elements. The computer, in response to identifying the function, identifies within a list of indica, at least one reference indicia corresponding to the at least one function. The computer identifies within a monolithic application relevant code elements associated with the reference indicia. The computer generates an activity log associated with execution of the relevant code elements. The computer identifies, within the activity log, a group of data elements associated with the execution of the relevant code elements. The computer generates a group data element clusters using a Machine Learning algorithm. The computer identifies at least one of the group of data element clusters as relevant to the at least one function.