Virtual Device Resource Generation for Unified BigData Integration
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Solution Overview
Problem
Current machine-to-machine and BigData platforms are fragmented, making it difficult to integrate and efficiently manage large volumes of data, such as Petabytes or Exabytes, for applications across various sectors like e-health and energy-saving, which hinders fast integration, filtering, and abstraction.
Innovation Solution
A method and system for generating virtual device resources that enable seamless integration of BigData and machine-to-machine applications by converting application requests into supported requests for BigData repositories, executing these requests to describe virtual device resources, and selecting and generating these resources, allowing for efficient data access and analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If BigData platforms and machine-to-machine platforms are operated separately, then each platform can be managed independently, but integration and unified data management become difficult
Solution Approach 1:
The patent combines BigData platform and machine-to-machine platform into a unified system where a single platform can handle both types of operations. The system integrates data storage, processing, and application execution capabilities from both platforms, enabling unified management while maintaining the functional characteristics of each platform type.
Solution Approach 2:
The patent creates a universal platform that can perform multiple functions: it can store and manage BigData, execute machine-to-machine applications, and provide both data processing and device control capabilities. This multi-functional approach eliminates the need for separate platforms while maintaining the specialized capabilities of each.
2Ease of operation
If conventional machine-to-machine technology is used, then simple device communication is achieved, but handling large volumes of BigData becomes difficult
Solution Approach 1:
The patent segments the system into distinct functional modules: a data storage module for BigData, a processing module for data mining and analytics, and an application execution module for machine-to-machine operations. This segmentation allows the system to handle both simple device communication and large-scale data processing simultaneously by distributing tasks across specialized components.
Solution Approach 2:
The patent adds a data dimension to the traditional machine-to-machine communication by integrating BigData storage and processing capabilities. This transforms the system from purely device-centric communication to a hybrid architecture that simultaneously handles device communication and large-scale data management, effectively adding a new operational dimension.
3Adaptability or versatility
If data is stored in fragmented BigData repositories, then data from multiple sources can be collected, but fast integration and filtering become problematic
Solution Approach 1:
The patent merges multiple fragmented BigData repositories into a unified data storage and processing system. By consolidating data from multiple sources into a single integrated platform, the system eliminates the need for complex cross-repository data integration while maintaining the ability to collect and process data from diverse sources efficiently.
Solution Approach 2:
The patent introduces an intermediary processing layer that mediates between multiple data sources and the application layer. This intermediary component handles data filtering, transformation, and integration tasks, enabling fast data processing by preprocessing and organizing data before it reaches the application execution layer.
4Reliability
If proprietary vertical-market solutions are used, then specific industry requirements are met, but broader application across multiple sectors is limited
Solution Approach 1:
The patent creates a universal platform architecture that can adapt to multiple industry sectors while maintaining reliable performance. The system provides standardized interfaces and processing capabilities that can be configured for different applications, from e-health to agriculture to energy management, eliminating the need for separate proprietary solutions for each sector.
Solution Approach 2:
The patent implements a dynamic configuration system that allows the platform to adapt its behavior and capabilities based on the specific application requirements. The system can dynamically adjust its data processing pipelines, application execution environments, and resource allocation to optimize performance for different industry sectors while maintaining a single unified platform.
Data Source
AI summary
A method for generating a virtual device resource (VDR) accessible by an application includes providing a request for discovery of information by one or more applications; converting the provided request into one or more requests for one or more BigData repositories, wherein the converted one or more requests are supported by the one or more BigData repositories and fit the provided request; transmitting the converted one or more requests to the one or more BigData repositories; executing the transmitted one or more requests resulting in one or more descriptions of virtual device resources; selecting one or more virtual device resources (VDR) based on the one or more descriptions; and generating the selected, one or more virtual device resources (VDR).