Ontology-Based Data Analysis Dashboard
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Solution Overview
Problem
Current data analysis systems lack an intuitive, object-oriented methodology for non-technical users to explore and perform ad hoc analysis on large data sets without requiring in-depth knowledge of underlying data tables and object associations, and there is no system to track and share analysis operations or visualizations effectively.
Innovation Solution
An ontology-based and content-based query system that allows users to interactively filter and visualize data objects on a dashboard, using predefined object types and properties, enabling users to select object types, properties, and values without needing to write code or understand complex data queries, and allowing for the saving and sharing of filtering operations and visualizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional data analysis systems are used, then data can be queried and analyzed, but users need in-depth knowledge of underlying data tables and object associations which increases the barrier to use
Solution Approach 1:
The patent introduces an intermediary layer between users and the complex data structure. This intermediary is implemented through: (1) An ontology browser that presents simplified object-type hierarchies and relationships, (2) A query builder that automatically translates user selections into proper database queries, and (3) A dashboard that provides high-level visualizations without exposing underlying table structures. This intermediary absorbs the complexity of data associations while presenting a simplified interface to users.
Solution Approach 2:
The patent segments the data analysis process into distinct, manageable components: (1) Ontology navigation for exploring data relationships, (2) Query building for filtering and selecting data, (3) Visualization for displaying results, and (4) Dashboard for summarizing insights. Each component handles a specific aspect of data exploration independently, making the overall system easier to navigate and use despite the complexity of the underlying data structure.
2Measurement precision
If detailed data queries are performed to explore specific portions of data sets, then analysis precision is improved, but the number of queries and time required increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining and pre-organizing data relationships in the ontology before users need to analyze the data. The ontology browser pre-maps object-type hierarchies and associations, allowing users to immediately navigate structured relationships without needing to perform complex join operations or explore table structures. This preliminary organization enables precise analysis through intuitive navigation rather than time-consuming querying.
Solution Approach 2:
The system provides feedback loops where users can navigate the ontology, select object types and properties, and immediately see filtered results displayed on the dashboard. This feedback mechanism allows users to refine their analysis iteratively by observing how selections filter the data and adjusting their queries accordingly, reducing the time needed to achieve precise analytical results compared to manual query construction.
3Loss of information
If visualizations are created to display data subsets, then data insight is improved, but there is no system to track and share analysis operations
Solution Approach 1:
The patent implements a universal dashboard component that serves multiple functions: (1) Displaying visualizations of data subsets, (2) Tracking the sequence of filtering operations applied, (3) Storing analysis parameters and configurations, and (4) Enabling sharing with other users. This multi-functional dashboard consolidates what would otherwise require separate systems, allowing users to both gain insights from visualizations and share their analysis workflows with colleagues.
Solution Approach 2:
The system creates a copy of the analysis workflow and dashboard configuration that can be shared with other users. When users save their analysis, the system generates a replicable representation of their filtering operations, selected object types, and visualization settings. This copy can be transmitted to other users who can then reproduce the same analysis on their systems, enabling knowledge transfer without requiring direct access to the original data or complex query knowledge.
Data Source
AI summary
Systems and methods for analyzing data stored using a data model. The system can receive a user selection of a first object type indicating to perform filtering operations on a first set of data objects, generate a list of object types linked to the first object type based on an ontology, receives a user selection of a second object type, generate a list of properties of the second object type based on an ontology, receive a user selection of a first property from the list of properties, perform a data query determining values associated with the first property, receive a user selection of a first value, and displays information of a subset of data objects being a portion of the first set of data objects that are linked to data objects in the second set of data objects that have a first property value of the first value.