IoT Data Processing with Precomputed Statistics and URI Routing
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Solution Overview
Problem
The oneM2M architecture faces inefficiencies in data processing due to high computational resource consumption, long processing times, and complex access control policies, particularly when handling large volumes of IoT data and requiring statistical calculations, which hinders timely data retrieval and storage.
Innovation Solution
A data processing system that separates and categorizes data based on type and URI location, using multiple perpetuation functions to efficiently store and manage data in a database, reducing computational load and simplifying access control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is fetched by designating a URI of a resource through the mca interface, then data can be retrieved and processed, but the application consumes many calculation resources and requires long time to obtain results when handling large volumes of data
Solution Approach 1:
The patent pre-calculates and stores statistical values (sum, count, average, variance, etc.) in the CSE database before applications request them. When an application needs statistical data, the pre-computed results are immediately available without requiring the application to perform heavy calculations on raw data, thus resolving the contradiction between data retrieval accuracy and processing speed.
Solution Approach 2:
The patent divides data processing into two separate components: (1) data collection and pre-calculation performed by the CSE, and (2) data retrieval and application processing. This segmentation allows the CSE to handle heavy computational tasks independently, reducing the burden on applications and improving overall system productivity while maintaining data accuracy.
2Adaptability or versatility
If statistical calculation functions are implemented in the application, then the application can perform required calculations, but the man-hour for implementation increases
Solution Approach 1:
The CSE automatically performs statistical calculations and maintains calculation results without requiring applications to implement their own calculation logic. The system serves itself by pre-computing statistical values and making them available through standard interfaces, thus providing adaptability while reducing implementation complexity for applications.
Solution Approach 2:
The CSE provides universal statistical calculation capabilities that can be used by any application through the standard mca interface. Instead of each application implementing its own calculation functions, the CSE offers a multi-functional service that handles various statistical operations (sum, count, average, variance, etc.) for different data types and time ranges, reducing overall system complexity.
3Quantity of substance
If data is stored in container resources and time-series resources in the CSE, then data can be accumulated and detected, but more processing time is required due to lack of effective indexes
Solution Approach 1:
The patent pre-organizes data by creating indexed structures in the CSE database that categorize data by resource type, time range, and statistical parameters. This preliminary organization allows for efficient retrieval without scanning all stored data, thus maintaining high storage capacity while reducing processing time for data access.
4Reliability
If access control policies are set for each data instance, then data security is improved, but the system complexity and processing time increase
Solution Approach 1:
The patent implements a unified access control mechanism in the CSE that applies to all data instances through a single policy configuration. Instead of managing separate access control policies for each data instance, the CSE provides universal access control that can be configured once and applied across multiple resources, maintaining security while reducing system complexity.
Data Source
AI summary
A data processing system according to an embodiment is a data processing system in which one or more application programs and one or more devices are connected to a database via a network, the system including a processor configured to: perform acceptance processing for accepting a request from the application programs or data from the devices; have a plurality of types of perpetuation functions of performing processing in accordance with a type of the request or the data and perpetually storing a processing result in the database, perform, using the perpetuation functions, processing corresponding to the type of the accepted request or data, and perform perpetuation processing for perpetually storing the processing result in the database; and perform switching processing for switching a destination at which the accepted request or data is processed to one of the plurality of types of perpetuation functions, based on the type of the accepted request or data.