Visual Data Importer for Dynamic Object Model Creation
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Solution Overview
Problem
Existing data storage systems are poorly suited for data analysis due to their rigid structure, necessitating a more efficient method to reorganize data into an object model that defines object structures and relationships.
Innovation Solution
A computing device displays object icons and relationship types in a GUI, allowing users to create and update an object model by mapping data sources to object properties and relationships, with the ability to visualize and manipulate these elements, and automatically generate objects and relationships based on user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If data is stored in fixed, rigidly structured data stores (such as flat files or relational databases), then data storage and organization are simplified, but data analysis efficiency deteriorates due to the mismatch between rigid structure and analytical needs
Solution Approach 1:
The patent implements a dynamic ontology system where the object model can evolve over time as needed for analysis. The ontology allows objects, properties, and relationships to be added, modified, or removed dynamically, enabling the data structure to adapt to changing analytical requirements rather than being constrained by fixed schemas. This resolves the contradiction by making the data organization flexible enough to support efficient analysis while maintaining structured organization.
Solution Approach 2:
The patent segments data into discrete objects with defined properties and relationships, organized through an ontology hierarchy. Rather than storing data in rigid tabular formats, the system divides data into modular object instances that can be independently manipulated and analyzed. This segmentation enables more efficient data analysis by allowing selective access and manipulation of specific object types and their relationships without being constrained by fixed table structures.
2Productivity
If data is reorganized according to an object model with ontology-based semantics, then data analysis efficiency improves, but system complexity increases due to the need to define and maintain object structures, properties, and relationships
Solution Approach 1:
The patent implements a universal ontology framework that can accommodate multiple object types, properties, and relationships through a common structural paradigm. The ontology defines reusable templates for objects, properties, and relationships that can be instantiated multiple times with different specific values. This universality reduces complexity by providing a standardized approach to modeling diverse data, rather than requiring custom structures for each data type.
Solution Approach 2:
The ontology serves as an intermediary layer between the raw data and the analysis processes. Rather than directly manipulating complex object models, users and analysis tools interact with the ontology-defined abstractions. The ontology mediates the complexity by providing a standardized interface for defining and querying object structures, properties, and relationships, shielding users from the underlying complexity of the object model implementation.
3Adaptability or versatility
If a dynamic ontology is used to allow the object model and semantics to change and evolve over time, then adaptability to different analysis needs improves, but the difficulty of detecting and measuring data relationships increases
Solution Approach 1:
The patent implements feedback mechanisms that track changes to the ontology and object model over time. The system maintains history of ontology evolutions, object creations, and relationship modifications, enabling users to detect and measure data relationships across different time points. This feedback capability allows the system to adapt to changing analysis needs while maintaining visibility into how data relationships have evolved, resolving the difficulty of tracking relationships in a dynamic environment.
Data Source
AI summary
Techniques for visual data import into an object model are described. A graphical user interface concurrently displays a first icon that represents a first object type and a second icon that represents a second object type. Input defining object-to-data mappings between properties of the object types and structured data of one or more data sources is received. Further input defining a relationship type for relationships between the first object type and the second object type is also received. In response to the second input, a graphical representation of the relationship type is displayed, visually linking the first icon to the second icon. Based at least on the object-to-data mappings, the definition of the relationship type, and the structured data, an object model is created, comprising first objects of the first object type, second objects of the second object type, and relationships between the first objects and the second objects.