Metadata Correlation for Interactive Data Visualization

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Solution Overview

Problem

Users face difficulties in leveraging large amounts of data from multiple sources due to the need for data modeling to establish correlations between datasets, which is cumbersome and requires skilled intervention, hindering the ability to create interactive information applications without manual data modeling.

Innovation Solution

A computer-implemented method that analyzes metadata from multiple datasets to derive correlation scores between attributes, determining matches and establishing relationships without the need for manual data modeling, allowing for automatic presentation of related data from different datasets.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual data modeling is used to establish correlations between datasets, then the accuracy of data relationships is improved, but the complexity of operation and time required increase significantly

Engineering Contradiction:

Improveaccuracy of data relationshipsVSAvoidcomplexity of operation

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system performs self-service by automatically analyzing metadata from multiple datasets, deriving correlation scores between attributes, and establishing relationships without requiring manual intervention. The computer device autonomously determines matches between attributes based on metadata comparison and correlation scoring, eliminating the need for skilled data modelers to manually establish data relationships.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical process of manual data modeling with an automated computational system. Instead of skilled professionals manually analyzing and connecting datasets, the system uses algorithmic metadata analysis, correlation score derivation, and automated relationship establishment to substitute human expertise with a systematic computational approach.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If manual data modeling is performed to establish dataset correlations, then the reliability of data relationships is improved, but the time required and productivity are worsened

Engineering Contradiction:

Improvereliability of data relationshipsVSAvoidtime required

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs preliminary action by automatically analyzing metadata and establishing correlations between datasets before any manual intervention or data modeling process. The computer device pre-processes the datasets, extracts metadata, derives correlation scores, and determines relationships in advance, eliminating the need for time-consuming manual data modeling while maintaining reliability through systematic automated analysis.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system performs self-service by autonomously completing the entire data relationship establishment process without human intervention. The computer device automatically compares metadata, derives correlation scores, determines attribute matches, and establishes dataset relationships, thereby eliminating the time required for manual modeling while maintaining reliable data relationships through consistent algorithmic processing.

Inventive Principle:

Principle #25Self-service

3Ease of operation

If automated metadata analysis is used to determine dataset relationships, then the ease of operation is improved, but the precision of correlation determination may be worsened

Engineering Contradiction:

Improveease of creating applicationsVSAvoidprecision of correlation scores

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system applies parameter changes by transforming metadata into correlation scores through a structured analytical process. The computer device changes the state of raw metadata into meaningful correlation metrics by comparing attributes across datasets, deriving numerical scores that represent the strength and nature of relationships, thereby maintaining precision while enabling automated operation.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces manual precision-based data modeling with an automated computational system that uses metadata analysis and correlation scoring. The system substitutes human expert judgment with algorithmic processing that consistently applies comparison criteria to derive correlation scores, maintaining determination precision through systematic automated analysis rather than subjective human assessment.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11269905B2Interaction between visualizations and other data controls in an information system by matching attributes in different datasets

Publication Date: 2022.03.08 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A computer-implemented method includes analyzing a first dataset to extract metadata that corresponds to a first visualization; analyzing a second dataset to extract metadata; comparing the metadata of the datasets; deriving based on the comparing, a level of correlation between attributes of the datasets; establishing a score for each of the levels of correlation; determining that a first attribute of the first dataset and a first attribute of the second dataset are a match in response to the establishing of a score for the level of correlation of the first attributes of the datasets; determining that the datasets are related in response to the determining that the first attributes of the datasets are a match; and directing the displaying of a second visualization, the second visualization being a visual representation that includes data from the second dataset.