Development of an Interactive Cognitive Mapping and Bayesian Network Simulation Platform

The client faces difficulties in efficiently creating and managing cognitive maps across various devices, ensuring scalable and flexible application architecture adaptable to evolving user requirements, and simplifying the creation and handling of decision tables within complex data environments for enterprise-level decision support.

A mid-to-large enterprise specializing in utility management and regulatory compliance, seeking advanced tools for process modeling, risk analysis, and decision support.

• Enable intuitive, interactive creation and management of cognitive maps with real-time feedback visualization.
• Develop scalable and flexible application architecture that supports future feature expansion and diverse user requirements.
• Implement user-friendly tools for designing and maintaining decision tables integrated with Bayesian network evaluations.
• Allow users to manually and automatically build Bayesian networks, including structure learning from data and parameter tuning.
• Incorporate robust simulation capabilities enabling users to input, edit, and visualize data-driven outcomes via embedded analytics tools.
• Ensure the platform handles large structured datasets efficiently and provides rich customization options for varied enterprise needs.

• Interactive cognitive map editor with visual relationship highlighting and expandable structures.
• User interface for manual creation and modification of Bayesian networks (nodes, links, parameters).
• Automated structure and probability table learning from user-supplied data via API integrations.
• Integration with external databases (e.g., SQL) for real-time data input and management within models.
• Simulation engine that executes models with live data inputs, providing results visualization through embedded analytics dashboards.
• Support for decision table evaluation based on Bayesian network assessments to facilitate complex decision-making processes.

• APIs for automated Bayesian network structure and probability learning from data
• Connections to enterprise data sources such as Microsoft SQL databases
• Embedded analytics tools like Power BI for visualization

• Scalability to support large datasets and multiple concurrent users
• High performance for real-time simulation and data processing
• Robust security protocols to ensure data integrity and user access control
• Responsive UI compatible across multiple devices and browsers
• High availability and fault tolerance for enterprise deployment

The platform is expected to significantly improve the efficiency of decision modeling by providing intuitive tools coupled with robust data integration and simulation capabilities, enabling users to build, evaluate, and optimize complex cognitive maps and Bayesian networks. The system aims to support enterprise needs for scalable, flexible, and user-friendly decision support solutions, leading to enhanced operational insights and risk mitigation across industries such as energy, government, and utilities.