Semantic Layer for Dynamic Spatial Data Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Analyzing spatial data from enterprise software systems is challenging due to the difficulty in relating analytical data to specific enterprise entities or activities without complex functional programming and materialization of results, which limits dynamic viewing of data from alternative perspectives.
Innovation Solution
A system architecture that dynamically projects data at the space level by generating a view model incorporating 'thing' dimensions, allowing for time-dependent logical relationships between spaces and things, enabling efficient slicing and dicing of data using semantic information, and enriching reporting with additional attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If conventional functional programming and materialization methods are used to relate analytical data to enterprise entities, then data analysis capability is improved, but system complexity and programming effort increase significantly
Solution Approach 1:
The patent introduces a semantic layer as an intermediary between the analytical data model and the user interface. This semantic layer contains predefined relationships between spaces and things, allowing users to query spatial data without needing to understand the complex underlying data model or write complex functional programming code. The semantic layer mediates between the raw analytical data and the user's analytical needs, simplifying the system's effective complexity while maintaining data analysis capability.
Solution Approach 2:
The patent creates a view model that is a simplified copy or representation of the complex analytical data model. Instead of requiring users to work directly with the complex materialized results and functional programming interfaces, the system provides a copied view through the semantic layer that preserves the essential analytical capabilities while eliminating the complexity of the underlying implementation details.
2Reliability
If complex functional programming is used to materialize analytical results, then data relationships are established, but development time and operational flexibility are reduced
Solution Approach 1:
The patent performs preliminary action by pre-defining the relationships between spaces and things in the semantic layer during system design and setup. Instead of requiring complex functional programming at runtime to establish data relationships, the semantic layer contains pre-configured associations, dimensions, and hierarchies that are ready to use. This preliminary configuration ensures data relationship accuracy while dramatically reducing development time and improving operational flexibility, as users can leverage these pre-established relationships without writing complex programming code.
3Stability of the object's composition
If data is viewed from traditional data model perspectives, then data consistency is maintained, but dynamic viewing from alternative perspectives is limited
Solution Approach 1:
The patent adds another dimension to data viewing by introducing the semantic layer with its own organizational structure based on spaces and things. Users can view the same analytical data from multiple perspectives: the traditional data model perspective and the semantic perspective organized around physical spaces and enterprise entities. This additional dimension allows dynamic viewing from alternative perspectives while maintaining data consistency, as the semantic layer maps to the underlying consistent data model through defined relationships.
Data Source
AI summary
A system includes determination of a first measure value associated with a first physical space and a first time period within the analytical data, dynamic determination of a time-dependent association between a first entity or event and the first physical, dynamic mapping of the first measure value to the first entity or event based on the time-dependent association, and presentation of the first measure value in association with the first entity or event.