Knowledge-Driven Architecture for Information System Lifecycle Development
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Solution Overview
Problem
The complexity of modern information system development lifecycles, exacerbated by cloud computing and emerging technologies, is hindered by poorly managed knowledge management systems that fail to store knowledge in a machine-consumable format, leading to inefficiencies and increased business risk.
Innovation Solution
A knowledge-driven architecture that includes a knowledgebase repository with a knowledge engine, an upstream subsystem for feeding and updating knowledge models, and a downstream subsystem providing features for information system development, featuring modules for engineering, architecture, and governance, which systematizes aspects like code generation, risk assessment, and project planning using ontology and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional knowledge management systems (wiki pages, architecture documentation repositories) are used to store information, then knowledge can be accessed by users, but the knowledge is not stored in a machine consumable format and cannot be automatically updated to reflect the actual state of the system
Solution Approach 1:
The patent transforms knowledge from human-readable formats (wiki pages, documentation) into machine-consumable formats through structured data models, ontologies, and formal representations. This parameter change enables automated processing while preserving knowledge accessibility through multiple interfaces.
Solution Approach 2:
The patent introduces a knowledge management system with knowledge graphs, ontologies, and structured data models as intermediaries between traditional documentation systems and automated development tools. This intermediary layer enables machine consumption of knowledge while maintaining compatibility with existing human-readable documentation.
2Reliability
If developers, architects, managers and executives manually acquire and assimilate knowledge of new and old concepts, then they can understand system requirements and design, but the process is time-consuming and reduces development speed
Solution Approach 1:
The patent replaces manual knowledge acquisition and assimilation processes with automated knowledge extraction, processing, and delivery systems. Machine learning models, natural language processing, and automated reasoning engines substitute human cognitive efforts, maintaining knowledge understanding while dramatically increasing development speed.
Solution Approach 2:
The patent enables the knowledge management system to automatically acquire, process, update, and distribute knowledge without human intervention. The system self-updates based on changes in the information system lifecycle, automatically serving developers, architects, and stakeholders with current knowledge.
3Ease of manufacture
If knowledge management systems are poorly managed, then implementation is simple, but significant business risk is created
Solution Approach 1:
The patent segments the knowledge management system into modular components including knowledge graphs, ontologies, data models, and integration layers. This segmentation allows incremental implementation and deployment while maintaining system reliability through isolated, manageable modules that can be independently validated and updated.
Solution Approach 2:
The patent implements feedback mechanisms where the knowledge management system continuously monitors the actual state of information systems, automatically updates knowledge representations, and validates consistency. This feedback loop reduces business risk by ensuring knowledge accuracy and reliability while maintaining ease of implementation through automated validation.
Data Source
AI summary
A knowledge driven architecture for information system lifecycle development is disclosed. The architecture includes a knowledgebase repository including a knowledge engine to store a first set of knowledge models associated with an information system development; an upstream subsystem to feed a second set of knowledge models to the knowledge engine and update the knowledge engine with a final set of knowledge models; a downstream subsystem to provide one or more features corresponding to the information system development to a user. The downstream subsystem includes an engineering knowledge module to systematise one or more aspects of an information system engineering lifecycle and includes a project scaffolding manager to generate code template, an architectural knowledge module to systematise one or more features associated with architecture and design lifecycle of the information system, a planning and governance module to systematise one or more features associated with information system planning and governance life cycle.