Graph-Based Data Model for Cloud Interoperability
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Solution Overview
Problem
Conventional approaches fail to effectively manage and utilize large amounts of enterprise data due to data silos, incompatibility of data management and analysis applications, and suboptimal mechanisms for discovering and summarizing data in cloud-based environments, leading to difficulties in interoperability and efficient data operations.
Innovation Solution
A computing platform configured to analyze and consolidate datasets using graph-based data arrangements, identifying relevant data and generating programmatic executable instructions to perform actions, such as creating concept interfaces, through a collaborative dataset consolidation system that correlates and deduplicates data across disparate sources, enabling efficient data operations and integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data storage technologies are used to store increasing amounts of generated data, then data capacity is improved, but data silos are created that segregate and isolate datasets
Solution Approach 1:
The patent merges disparate datasets from different computing platforms and database technologies into a unified data structure that eliminates data silos. The system combines data from multiple sources while maintaining the ability to query and analyze them collectively, thus improving both data capacity and interoperability simultaneously.
Solution Approach 2:
The patent creates a universal data interface that can work with multiple database technologies and data formats. This universal interface allows the system to handle diverse data sources without creating segregation, enabling single-point access to heterogeneous data while maintaining compatibility across different platforms.
2Measurement precision
If data scientists create complex data models using sophisticated analysis application tools, then data analysis capability is improved, but interoperability with other analytic data tools is frustrated
Solution Approach 1:
The patent introduces an intermediary layer that sits between sophisticated data analysis tools and the underlying data models. This intermediary interface allows complex data models to be created and analyzed with high precision while providing standardized access points that enable interoperability with other analytic tools, thus resolving the compatibility frustration.
Solution Approach 2:
The patent segments the data analysis system into distinct layers: complex data model creation layer for sophisticated analysis, and a standardized interface layer for interoperability. This segmentation allows data scientists to work with complex models using specialized tools while the standardized interface ensures compatibility with other analytic data tools.
3Adaptability or versatility
If manual intervention is required to apply derived formulaic data models to datasets using local computing resources, then data model flexibility is improved, but operational burden and maintenance complexity increase
Solution Approach 1:
The patent implements self-service capabilities where the system automatically applies derived formulaic data models to datasets without requiring manual intervention. The system autonomously manages data model application, updating, and maintenance while preserving the flexibility to work with diverse data formats and structures, thus reducing operational burden while maintaining adaptability.
4Ease of operation
If conventional mechanisms are used to discover and summarize vast amounts of data in cloud-based environments, then data accessibility is improved, but discovery and summarization effectiveness become suboptimal
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing vast amounts of cloud-based data into structured formats with embedded metadata and relationships. This preliminary organization enables efficient discovery and summarization mechanisms to quickly locate and analyze relevant data without sacrificing accessibility, thus improving discovery effectiveness while maintaining ease of access.
Data Source
AI summary
Various embodiments relate generally to data science and data analysis, computer software and systems, and data-driven control systems and algorithms based on graph-based data arrangements, and, more specifically, to a computing platform configured to receive and analyze datasets to implement a data model with which to identify relevant data catalog data derived from graph-based data arrangements, whereby a processor may be configured to cause implementation of subsets of programmatic executable instructions based on relevant data catalog data, at least one example of which generates a concept interface portion adapted to an associated concept, among other things. In some examples, a method may include identifying attributes associated with data values linked to a conceptual entity, determining criteria data of a targeted application, selecting programmatic executable instructions based on the criteria data, and extracting data from a graph data arrangement as a function of the attributes, among other things.