Dynamic Ontology Curation for Sensor Data
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Solution Overview
Problem
The inflexibility of traditional database ontologies poses challenges in curating and managing high-scale datasets, particularly when dealing with sensor data that requires dynamic structure adjustments and varied user perspectives.
Innovation Solution
A network-based ontology curation system that generates and curates ontologies on-the-fly based on request attributes, including device and user characteristics, using machine learning techniques to prioritize and present relevant data objects, allowing for flexible data presentation across different user groups and devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed ontology structure is used in traditional database systems, then data organization is simplified and management is easier, but the system becomes inflexible and cannot adapt to high-scale datasets or dynamic user needs
Solution Approach 1:
The patent implements dynamic ontology generation where the system automatically creates and adjusts ontology structures based on user characteristics, device attributes, and data properties. Instead of using a static fixed ontology, the system dynamically adapts the ontology to match specific user needs and data characteristics, resolving the contradiction between ease of management and adaptability.
Solution Approach 2:
The system changes ontology parameters dynamically by generating different ontology structures based on varying user profiles, device types, and data scales. This allows the same underlying data to be organized and presented differently for different users without requiring manual reconfiguration, thus maintaining ease of operation while achieving high adaptability.
2Reliability
If manual user intervention is required to modify database schemas or create new tables, then data accuracy and control are improved, but time consumption and operational complexity increase
Solution Approach 1:
The system performs self-service by automatically generating ontologies and data structures based on user profiles and data characteristics without requiring manual administrator intervention. The automated ontology generation process maintains data control accuracy through algorithmic consistency while eliminating the time loss associated with manual schema modifications.
Solution Approach 2:
The system performs preliminary action by pre-defining ontology generation rules and algorithms that automatically adapt to different user needs. This preliminary setup allows the system to quickly generate appropriate ontologies without manual intervention, reducing time loss while maintaining reliability through pre-established data management protocols.
3Quantity of substance
If traditional database systems store and manage high-scale sensor data, then data storage capacity is sufficient, but data retrieval efficiency and user-specific presentation deteriorate
Solution Approach 1:
The system segments the monolithic database structure into user-specific ontology-based views. By organizing data according to user profiles and device attributes, the system efficiently retrieves and presents only the relevant data subsets for each user, improving productivity while maintaining the ability to store large volumes of sensor data through the underlying database infrastructure.
Data Source
AI summary
Example embodiments relate to a network-based ontology curation system employed for receiving a request to view a data object, curating an ontology associated with the data object on-the-fly based on attributes of the request that include device and user characteristics.