Predictive Graph Generation Using NoSQL Data Accumulation
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Solution Overview
Problem
Current data management systems struggle with efficiently processing large volumes of data for real-time analytics and predictive modeling, particularly in big data repositories, due to limitations in handling unstructured data and rapid data growth, leading to intractable data management and prediction challenges for enterprises.
Innovation Solution
A big data analysis system comprising a data accumulation server, predictive modeling system, and predictive graph processing system that utilizes graph-based notation and big data technologies to transform and analyze data from various sources, enabling near real-time processing and generation of predictive graphs for user presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If non-big data technologies (relational databases, columnar databases) are used for data processing, then structured data analysis can be performed, but the system is limited in handling unstructured data and log data, and requires expensive data management support
Solution Approach 1:
The patent creates a simplified copy of the complex graph data structure by representing it as a collection of key-value pairs in a NoSQL database. Instead of implementing a full graph database with all its complexity, the system copies the essential functionality by storing vertices and edges as simple document structures, eliminating the need for expensive graph database management systems while preserving the core predictive analytics capability
Solution Approach 2:
The patent replaces the mechanical complexity of traditional graph database systems with a software-based solution running on standard NoSQL database infrastructure. By substituting the specialized graph database engine with a combination of NoSQL database and predictive analytics software, the system eliminates expensive hardware and software requirements while maintaining the ability to process graph-based predictive models
2Productivity
If traditional data management systems are used, then data can be stored, but real-time processing of large-volume data repositories for predictive modeling is inefficient and cannot handle rapid data growth
Solution Approach 1:
The patent changes the fundamental parameters of the database system by transitioning from relational databases with fixed schemas to NoSQL databases with flexible document structures. This parameter change allows the system to accommodate variable data formats from multiple sources (social media, logs, structured data) and enables efficient querying for predictive analytics by storing data in a format optimized for rapid retrieval and processing
Solution Approach 2:
The patent creates a universal data processing platform that can handle multiple data types (structured, unstructured, log data) from diverse sources through a single NoSQL database interface. The system uses a unified data model that can represent different data formats as flexible document structures, allowing the same infrastructure to process social media data, application logs, and structured business data without requiring separate specialized systems
3Adaptability or versatility
If data silos and relational databases are used for predictive analytics, then existing infrastructure can be utilized, but the system lacks variety of data handling and poses strict limits on data growth rates and volumes
Solution Approach 1:
The patent implements a dynamic data architecture where the database schema is not fixed but can adapt to new data types and structures as they emerge. The NoSQL document model allows fields and structures to be added or modified without system reconfiguration, enabling the platform to dynamically accommodate new data sources and formats as the enterprise grows, rather than being constrained by predetermined schema limitations
Data Source
AI summary
A big data analysis system may include a big data repository communicatively coupled to a data accumulation server and a predictive graph processing system. The data accumulation server may be configured to receive information from a plurality of data sources, the information corresponding to user interaction with one or more computing devices associated with an organization via a networked computing system, store the information received from the plurality of sources in the big data repository; and monitor the plurality of data sources to update the data stored in the big data repository. The predictive graph processing system is configured to receive information stored in the big data repository, transform the information received from the big data repository into a predictive graph data set based on a predictive model, and store the predictive graph data set to a visualization data repository.