Action-Based Logical Data Model for Dynamic Schema Adaptation
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Solution Overview
Problem
Conventional database systems face challenges in dynamically adapting to expanding data sets and new types of data, leading to issues like data inconsistencies, duplications, and the need for frequent model adaptations, especially when handling structured, unstructured, and semi-structured data across different data sources and models.
Innovation Solution
The implementation of an action-based logical data model that categorizes data elements into subject, object, spatial, and temporal categories, allowing for flexible storage and retrieval without predefining a complete schema, using unique identifiers and topology combinations to associate data elements with actions, enabling efficient storage and querying of structured, unstructured, and semi-structured data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a conventional relational database model with predefined schema is used, then data consistency and structure are maintained, but adaptability to new data types and expanding data sets is poor
Solution Approach 1:
The patent implements a dynamic data model where the schema is not fixed beforehand but is constructed automatically based on the data itself. The system dynamically identifies data elements, their relationships, and structures them into categories (subject, object, spatial, temporal) without requiring predefined tables or columns. This allows the database to adapt to new data types as they emerge while maintaining consistency through automated schema generation.
Solution Approach 2:
The patent uses the concept of copying data elements with unique identifiers to represent their meanings and relationships. Instead of requiring a predefined schema, the system creates copies of data elements and their relationships as they are encountered, building the database structure incrementally. This copying mechanism allows flexible representation of new data types without modifying the underlying data storage structure.
2Productivity
If a complete conceptual data model is created beforehand, then data structure is well-defined, but the process is time-consuming and requires frequent adaptations as data expands
Solution Approach 1:
The patent performs preliminary actions by automatically identifying and categorizing data elements as they are ingested, rather than requiring a complete pre-defined model. The system preliminarily processes data to extract meanings, relationships, and structures them into appropriate categories, which then forms the basis for the database schema. This eliminates the need for time-consuming manual model creation and adaptation.
Solution Approach 2:
The system performs self-service by automatically generating its own schema based on the data it receives. Instead of requiring external intervention to define the data model, the system analyzes the incoming data, identifies patterns and relationships, and autonomously constructs the appropriate database structure. This self-service capability dramatically reduces the time and effort required for model creation and adaptation.
3Adaptability or versatility
If data elements are stored without predefined labels or spaces, then flexibility and adaptability are improved, but data consistency and meaningful storage are compromised
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously analyzes stored data elements, their relationships, and patterns to refine and update the database schema. As new data is ingested, the system provides feedback about its structure and meaning, automatically adjusting the schema to maintain consistency. This feedback loop ensures that flexibility in storing new data types does not compromise data consistency, as the schema evolves to accommodate new patterns while maintaining established relationships.
Solution Approach 2:
The system changes parameters dynamically by adjusting data categorization and relationship definitions based on the actual data patterns observed. Instead of fixed schema parameters, the system modifies categorical assignments, relationship types, and structural parameters as new data types and relationships are discovered. This parameter change capability allows the system to maintain data consistency by adapting the storage parameters to match the actual data characteristics.
4Adaptability or versatility
If conventional database systems are used for diverse data types, then structured data is well-supported, but unstructured and semi-structured data cannot be effectively stored or queried
Solution Approach 1:
The patent creates a universal data model that can handle multiple data types (structured, unstructured, semi-structured) through a common framework of data elements, relationships, and categories. The same basic mechanisms for identifying data elements, establishing relationships, and organizing data work across all data types. This universality allows the system to store and query diverse data without requiring separate handling mechanisms for each type, maintaining ease of operation while expanding versatility.
Data Source
AI summary
A computer implemented and computer controlled method of arranging, in memory, data subsets retrieved from a single or from different data sources, and structured in accordance with a logical data model, for the processing of these data subsets by an action-based logical data model. The action-based logical data model comprises actions, data categories, including a subject data category, an object data category, a spatial data category and a temporal data category, action topology combinations, instance information supplemented to an action topology combination, and constructors. A constructor comprises a plurality of properties in accordance with a constructor topology combination. A property operates on action topology combinations matched to data subsets elements of data subsets in accordance with the action-based logical data model, for presenting structured relations between data subset elements of data subsets.