Parallel Data Chunk Decomposition for Real-Time Analysis
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Solution Overview
Problem
Current data analysis systems are inefficient in handling both large amounts of stored data and real-time streaming data, often requiring manual curation and lacking flexibility, especially when dealing with non-uniform data formats and irrelevant data, and they struggle to support serverless applications with real-time data-handling capabilities.
Innovation Solution
A system and method for parallel decomposition and restoration of data chunks, utilizing a directed computation graph service module and a decomposable transformer service module to analyze input data, decompose it into chunks, and perform transformations in parallel, allowing for efficient processing and storage, and enabling easy modification and distribution of data analysis models across devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is processed manually and sequentially, then data analysis accuracy can be maintained, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent divides data processing into multiple independent worker processes that can operate in parallel. Each worker handles a portion of the data or a specific transformation task, allowing simultaneous processing of multiple data chunks without compromising analysis accuracy through the use of declarative formalism for specifying data analysis and transformation tasks.
Solution Approach 2:
The system performs preliminary analysis of input data to determine the best course of action and identifies decomposition opportunities before actual processing begins. This preliminary step allows the system to plan parallel processing workflows and prepare transformation pipelines in advance, reducing overall processing time while maintaining accuracy.
2Device complexity
If a single system is used for both batch data analysis and real-time streaming data, then system simplicity is maintained, but performance and efficiency deteriorate
Solution Approach 1:
The patent creates a universal data processing platform that can handle both batch data analysis and real-time streaming data through a common architecture. The system uses a directed computation graph service module and decomposable transformer service module that can process different data types and formats uniformly, achieving multi-functionality without requiring separate specialized systems.
Solution Approach 2:
The system dynamically adapts its processing mode based on the nature of the input data. It can switch between batch processing and streaming processing modes, and dynamically determine the best course of action for analyzing input data. This dynamic behavior allows a single system to efficiently handle diverse data processing requirements.
3Measurement precision
If data analysis models are created manually and customized for each application, then model accuracy can be optimized, but development time and complexity increase
Solution Approach 1:
The patent enables easy copying and distribution of data analysis models across multiple devices and applications. Once a model is created and validated, it can be replicated and deployed uniformly across the network, maintaining accuracy while reducing development time. The system supports model sharing and distribution through its distributed computing architecture.
Solution Approach 2:
The system allows for parameterized model configurations that can be adjusted through declarative formalism specifications. Rather than manually recreating models, users can modify parameters and transformation rules to adapt models to different applications, maintaining accuracy while simplifying the creation process.
4Reliability
If software applications are modified on each device individually, then device-specific optimization is achieved, but resource utilization and update efficiency deteriorate
Solution Approach 1:
The patent merges the model management functionality across multiple devices into a centralized or distributed model registry. Model updates and modifications are performed once and automatically propagated to all target devices, eliminating the need for individual device modifications. This approach maintains device-specific optimization through targeted deployments while dramatically improving resource utilization and update efficiency.
Data Source
AI summary
A system for parallel decomposition and restoration of data chunks is provided, wherein a decomposable transformer service module analyzes and decomposes data into data chunks and transformations that restore the original data from the chunks, enabling efficient storage, modification, and restoration of program code across a number of target devices using a central repository.