Adaptive Data Ingestion and Mapping (ADIM) in Dynamic Decision Systems

A method for adaptive data ingestion and mapping, comprising:

• receiving and parsing data from heterogeneous sources without requiring predefined schemas, wherein the data comprises structured, semi-structured, and unstructured formats;
• identifying entities, attributes, and relationships within the parsed data;
• aligning the entities, attributes, and relationships with an ontological structure using rule-based processing, machine learning, and natural language processing to resolve semantic variations across sources;
• refining and standardizing the aligned data based on historical patterns, domain-specific rules, and real-time operational context, wherein transformations are adjusted dynamically in response to changes in data;
• detecting changes in incoming data and applying differential updates to previously processed data, modifying only affected portions while maintaining computational efficiency;
• organizing normalized data into a graph format establishing contextual relationships between entities;
• and
• mapping said relationships to visualization elements for presentation to decision-support systems.

A non-transitory computer-readable medium comprising stored instructions, the instruction comprising instructions that when executed by a processor system, cause the processor system to:

• receive and parse heterogeneous data from multiple sources without requiring predefined schemas, the data including structured, semi-structured, and unstructured formats;
• identify entities, attributes, and relationships within the parsed data;
• align the identified entities, attributes, and relationships with an ontological structure using rule-based processing, machine learning, and natural language processing to resolve semantic variations across sources;
• refine and standardize the aligned data based on historical patterns, domain-specific rules, and real-time operational context, with transformations adjusted dynamically in response to changes in the data structure or content;
• detect changes in incoming data and apply differential updates to previously processed data, modifying only affected portions while maintaining computational efficiency;
• organize normalized data into a graph-based representation establishing contextual relationships between entities;
• and
• map said relationships to visualization elements for presentation to decision-support systems.