Service-Layer Data Analytics Interface for Uniform IoT Access
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Solution Overview
Problem
Existing M2M/IoT systems lack a common data analytics service at the service layer, which impedes the extraction of intrinsic information from unstructured data and hinders uniform access to proprietary data analytics services, leading to inefficiencies in data utilization and resource sharing.
Innovation Solution
A common Data Analytics Service (DAS) is introduced at the service layer, providing uniform access to various data analytics operations through a standardized interface, leveraging existing technologies and enabling capabilities like basic data statistics, image processing, and information extraction, while abstracting underlying complexities from service layer entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a common Data Analytics Service is introduced at the service layer, then uniform access to data analytics operations is improved, but device complexity increases
Solution Approach 1:
The patent introduces a common Data Analytics Service (DAS) at the service layer that acts as an intermediary between service layer entities and proprietary data analytics services. The DAS provides a standardized interface that abstracts the complexity of multiple proprietary services, allowing uniform access to data analytics operations without requiring service layer entities to directly interact with complex proprietary systems.
Solution Approach 2:
The common Data Analytics Service is designed to provide universal access to multiple types of data analytics operations including basic data statistics, image processing, and information extraction. By consolidating these diverse functionalities into a single service interface, the system achieves multi-functionality while maintaining simplicity for service layer entities.
2Productivity
If proprietary data analytics services are accessed directly, then data analytics capability is improved, but adaptability decreases
Solution Approach 1:
The common Data Analytics Service serves as a mediator that enables service layer entities to access multiple proprietary data analytics services through a single standardized interface. This intermediary layer translates uniform service layer requests into appropriate proprietary service calls, thereby maintaining adaptability while preserving access to diverse analytics capabilities.
Solution Approach 2:
The patent segments the data analytics functionality into two distinct layers: a standardized common interface at the service layer and multiple proprietary implementation services below. This segmentation allows service layer entities to interact with a simple, adaptable interface while the underlying proprietary services provide the actual analytics processing capability.
3Productivity
If data analytics operations are performed at the service layer, then resource sharing is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple data analytics resources and services into a unified common Data Analytics Service at the service layer. By combining access to basic data statistics, image processing, information extraction, and other analytics capabilities into a single service entity, the system improves resource sharing efficiency while managing complexity through a consolidated interface.
Data Source
AI summary
A common Data Analytics Service (DAS) at service layer is designed to use underlying existing/future data analytics technologies or tools and provide them to service layer entities that need those data analytics operations with uniform access approach. A general operation framework/interface design is used for enabling DAS and the operation details within DAS. Related procedures for interacting with DAS, including the new parameters in service layer request/response messages can be used.