CPS-G Graph Data Model for Real-Time Multi-Format Processing
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Solution Overview
Problem
Existing data processing systems face inefficiencies in handling real-time data ingestion, transformation, validation, and analysis, particularly in scenarios where data formats are misaligned or unstructured, leading to suboptimal performance and increased computational requirements.
Innovation Solution
A data processing system utilizing a graph-based data model, specifically a Compact Pattern Stream Graph (CPS-G), enables real-time or near real-time data processing through dynamic data model transformation, validation, and error correction, supporting multiple data types and formats, and facilitating efficient pattern extraction and distribution to networked devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional ETL data transformation methods are used to handle misaligned data formats, then data structure compatibility is improved, but processing time and computational complexity increase significantly
Solution Approach 1:
The patent transforms data into a graph-based representation where data elements become nodes and relationships become edges. This parameter transformation allows heterogeneous data formats to be unified under a common graph structure, eliminating the need for format-specific transformation logic and significantly reducing processing time while maintaining adaptability across different data sources
Solution Approach 2:
The graph data structure serves as an intermediary representation layer between diverse data sources and the processing system. By converting all incoming data into this universal graph format with standardized nodes and edges, the system achieves format compatibility without traditional ETL transformation overhead, enabling real-time processing of multi-format data streams
2Reliability
If comprehensive data validation and error detection are performed on all incoming data, then data quality is improved, but processing speed decreases
Solution Approach 1:
The patent performs preliminary validation by establishing schema definitions and constraints during graph construction. Data elements are validated against predefined patterns and relationships as they are ingested and transformed into the graph structure, rather than performing comprehensive validation later. This preliminary action ensures data quality while maintaining high processing speed throughout the pipeline
Solution Approach 2:
The validation process is segmented into multiple lightweight checks distributed throughout the data ingestion and graph construction pipeline. Rather than performing a single comprehensive validation pass, the system applies targeted validation rules at specific stages (ingestion, transformation, query), reducing the computational burden at any single point while maintaining overall data quality
3Speed
If real-time data processing is implemented for streaming data, then responsiveness is improved, but system complexity and resource requirements increase
Solution Approach 1:
The graph data structure serves multiple functions simultaneously: it acts as the data storage format, the transformation target, the validation framework, and the query execution model. This multi-functionality eliminates the need for separate processing systems for each operation, reducing overall system complexity while enabling real-time processing of streaming data with unified architecture
4Measurement precision
If data is stored in traditional relational formats for processing, then query capability is improved, but real-time analytics performance deteriorates
Solution Approach 1:
The patent transitions from traditional two-dimensional relational tables to a multi-dimensional graph structure where data elements, relationships, and metadata coexist in a unified namespace. This dimensional transformation enables direct traversal of relationships and patterns across the data set, providing both precise query capabilities and real-time analytics performance by eliminating the need for complex joins and aggregations required in relational systems
Data Source
AI summary
A method for processing of a data stream using a graph-based data model includes receiving a data stream including data messages; disassembling the data messages data elements and metadata; generating a structured data model comprising the set of data elements based on the type of the data elements and the pattern of the data messages; instantiating a workflow to process the structured data model; configuring a CPS-G model sub-graph to add to the CPS-G model based on the type of the data elements and the pattern of the data messages; adding the CPS-G model sub-graph to the CPS-G model to form the CPS-G dataset; and storing the CPS-G dataset including the CPS-G model sub-graph in a data store for further processing by streaming computation and machine learning algorithms.