Multi-Dimensional Data Resource Structures for IoT Storage
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Solution Overview
Problem
The existing oneM2M framework does not adequately represent multi-dimensional data and lacks efficient storage management for IoT data streams, particularly time series data, which leads to storage inefficiencies and scalability issues.
Innovation Solution
Proposed resource structures and attributes, such as 'SamplingPeriodCovered', allow for the representation of multi-dimensional data and enable efficient storage management by indicating the time interval for which data is stored, enabling users to configure data retention policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional oneM2M framework is used for data storage, then basic data management is supported, but multi-dimensional data representation and storage efficiency deteriorate
Solution Approach 1:
The patent segments multi-dimensional data into distinct dimensional components (e.g., time, location, device attributes) that can be independently managed and stored. Each dimension is represented as a separate entity with its own attributes, allowing efficient storage and retrieval without requiring complex monolithic data structures.
Solution Approach 2:
The patent introduces explicit dimensional attributes (time, location, device characteristics) as separate organizational layers in the data structure. This dimensional approach transforms flat data storage into multi-layered hierarchical storage, improving efficiency by enabling targeted queries and reduced data retrieval overhead.
2Reliability
If data retention periods are extended to improve data availability, then data access reliability improves, but storage resource consumption increases
Solution Approach 1:
The patent implements dynamic data retention policies where data is automatically archived or deleted based on configurable time parameters and usage patterns. The system dynamically adjusts storage allocation and retention periods for different data types and dimensions, ensuring critical data remains accessible while non-critical data is efficiently managed.
Solution Approach 2:
The patent introduces configurable time parameters (e.g., retention periods, archiving thresholds) that control data lifecycle management. By changing these parameters, the system can optimize between data availability and storage consumption without requiring manual intervention or complex policy frameworks.
3Loss of information
If comprehensive data collection is implemented to improve analytics capability, then data completeness improves, but storage requirements and processing complexity increase
Solution Approach 1:
The patent segments data collection into dimension-specific streams (time-series data, location data, device attributes) that can be independently processed and stored. This segmentation maintains data completeness while reducing overall management complexity by allowing specialized handling for each data type.
Solution Approach 2:
The patent creates a universal dimensional data structure that can accommodate multiple data types and sources through a common framework. This multi-functional structure handles diverse data (sensor readings, metadata, operational data) uniformly, reducing complexity compared to separate management systems for each data type.
Data Source
AI summary
Mechanisms for multidimensional data modeling and operations and related procedures are described. Resource structures for multidimensional data can be used. This can allow lumped operations such as RESTful operations and procedures on the multidimensional data. A new attribute “SamplingPeriodCovered” can be used to indicate the time interval when the related time series data (or any multi-dimension data streams) are stored. This can reduce the total size of the data stored.