Graph Data Arrangement Tool for Dataset Interoperability
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Solution Overview
Problem
Conventional data storage and computing technologies face challenges in interoperability among disparate datasets due to different formats and platforms, leading to data silos that are difficult to scale and analyze, especially with the growth of graph-based data structures, which complicates the matching and joining of datasets.
Innovation Solution
A computerized tool system that uses a collaborative dataset consolidation platform to identify and classify data subsets within graph data arrangements, enabling the determination of classification types and degrees of similarity to facilitate the joining of datasets across different formats and platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data storage and computing technologies are used to store and access datasets, then data can be stored in different formats and platforms, but data silos are created that are difficult to scale and analyze due to lack of interoperability
Solution Approach 1:
The patent introduces graph-based data structures as an intermediary layer between disparate datasets. These graph structures serve as a universal language that can represent relationships across different data formats and platforms, enabling interoperability without requiring direct integration between all data sources. The graph model acts as a mediator that translates and connects heterogeneous data silos.
Solution Approach 2:
The patent implements a universal data access interface that can handle multiple data formats and platforms through a single unified approach. This interface provides multi-functional capabilities to query, analyze, and access data across different sources using consistent methods, eliminating the need for separate access mechanisms for each data silo.
2Adaptability or versatility
If graph-based data structures are used to represent complex relationships, then data interoperability improves, but the complexity of matching and joining datasets increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and normalizing data during the ingestion phase. Data is transformed into graph structures upfront, with relationships and attributes standardized before analysis occurs. This preliminary structuring makes subsequent matching and joining operations significantly easier, as the complex work of data integration is performed once during ingestion rather than repeatedly during analysis.
Solution Approach 2:
The patent segments the data matching process into distinct phases: data ingestion and graph construction, similarity computation, and result retrieval. By dividing the complex matching task into these manageable segments, the system can optimize each phase independently and reduce the overall difficulty of dataset matching and joining.
3Measurement precision
If manual intervention is used to identify related datasets in graph-based arrangements, then data accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically identify and match related datasets through algorithmic similarity computations. The graph-based structure allows the system to autonomously traverse relationships, compute similarities, and identify connected datasets without requiring manual intervention, thereby maintaining accuracy while dramatically improving processing speed.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational mechanisms. Instead of human analysts manually examining and connecting datasets, the system uses automated graph traversal algorithms and similarity computations to identify relationships, substituting mechanical human effort with efficient computational processes that maintain accuracy while increasing productivity.
Data Source
AI summary
Various embodiments relate generally to data science and data analysis, computer software and systems, and wired and wireless network communications to interface among repositories of disparate datasets and computing machine-based entities configured to access datasets, and, more specifically, to a computing and data storage platform to implement computerized tools to identify data classifications and similar subsets of graph-based data arrangements with which to join, according to at least some examples. For example, a method may include determining a classification type for a subset of data based on a graph data arrangement, generating presentation data as a first user input to detect selection of the first user input. The method may include predicting a classification type for data. The method may also include generating other presentation data configured to join datasets, such as a column of tabular-formatted data with one or more portions of a graph data arrangement.