Dynamic Semantic Models with Multi-Index Data Retrieval
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Solution Overview
Problem
Organizations face challenges in effectively utilizing vast amounts of data due to its sheer quantity and disparate formats, leading to hidden insights and correlations going unnoticed, as employees may not be aware of the purpose or content of the data stored across the organization.
Innovation Solution
The development of dynamic semantic models with multiple indices that generate a raw data graph from source data, map it to a concept graph, and index model-identifiers (MIDs) based on content-types, enabling efficient data organization and retrieval through optimized indexing for text, time, and geo-spatial information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in disparate formats and locations across the organization, then data collection capacity increases, but data accessibility and usability deteriorate
Solution Approach 1:
The patent segments data into structured components using a semantic model with multiple indices (text index, time index, geo-spatial index). Each index handles specific data types independently, allowing the system to manage large quantities of disparate data while maintaining accessibility through targeted queries on specific index types.
Solution Approach 2:
The patent introduces a semantic model as an intermediary layer between raw data sources and user queries. This model includes model identifiers (MIDs) that map to specific data elements across disparate sources, enabling unified access to distributed data without requiring changes to the underlying storage structure.
2Quantity of substance
If the sheer quantity of data increases, then information coverage improves, but analysis efficiency deteriorates
Solution Approach 1:
The patent divides the data analysis task into segmented operations on different indices. Instead of scanning all data for every query, the system routes queries to specific indices (text, time, geo-spatial) based on query type, dramatically improving analysis efficiency while maintaining comprehensive information coverage.
Solution Approach 2:
The patent performs preliminary organization of data into structured indices during the data ingestion phase. By pre-processing and indexing data according to its semantic properties before analysis occurs, the system enables efficient retrieval and analysis without sacrificing information coverage.
3Adaptability or versatility
If data is stored in different formats and locations, then data source versatility increases, but data understanding deteriorates
Solution Approach 1:
The patent creates a universal semantic model that can represent data from multiple sources and formats through a common structure. The model identifiers and multiple indices provide a unified framework that maintains the versatility of different data sources while preserving meaningful information through standardized semantic relationships.
Data Source
AI summary
Embodiments are directed towards dynamic semantic models having multiple indices. Source data may be provided to a network computer from at least one separate data source. A raw data graph may be generated from the source data such that the structure of the raw data graph may be based on the structure of the source data. Elements of the raw data graph may be mapped to a concept graph. Concept instances may be generated based on the concept graph, the raw data graph, and the source data. Model-identifiers (MIDs) that correspond to the concept instances may be generated to include at least a path in the concept graph The MID values may be indexed into a plurality of indices based on a content-type of the data associated with the MIDs. In response to a query, a result set may be generated that includes result MIDs.