Visual Data Querying via Dimensionality Reduction
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Solution Overview
Problem
Current data querying methods, particularly using Structured Query Language (SQL), require expertise and are limited by the database schema, making it difficult to visualize and query higher-dimensional data effectively, as they do not facilitate intuitive exploration and selection of complex data patterns.
Innovation Solution
A system and method for visual construction of operations that render user interfaces for higher-dimensional data, allowing users to interact with multiple views of reduced dimensionality, enabling selection and querying of data points across different representations, and generating structured query language requests based on user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SQL is used for data querying, then data can be selected from multiple tables through relational techniques, but the method requires professional training and is limited by database schema knowledge
Solution Approach 1:
The patent introduces an intermediary layer between the user and the database schema. This intermediary translates natural language or visual selections into SQL queries automatically, eliminating the need for users to learn SQL syntax and database schema structures. The system acts as a mediator that handles the complexity of relational queries while presenting a simplified interface to end users.
Solution Approach 2:
The patent replaces the mechanical system of manual SQL writing and schema navigation with an automated query generation system. Instead of requiring users to manually construct SQL statements based on their knowledge of database schemas, the system automatically generates appropriate queries based on user selections from visual interfaces or natural language inputs.
2Loss of information
If higher-dimensional data is visualized, then more data aspects can be displayed, but the data becomes difficult to visualize and conceptualize
Solution Approach 1:
The patent applies dimensionality reduction techniques to transform high-dimensional data into lower-dimensional visual representations that can be displayed on standard screens. By projecting data from thousands of dimensions into 2D or 3D visual spaces, the system preserves the essential patterns and relationships while making the data visually accessible. This involves mathematical transformations that maintain the structural integrity of the data while reducing its visual complexity.
3Adaptability or versatility
If multiple views of reduced dimensionality are generated, then pattern recognition can be conducted across different representations, but the system complexity increases
Solution Approach 1:
The patent segments the complex task of high-dimensional data exploration into multiple manageable views, each focusing on different aspects or dimensions of the data. Instead of presenting all dimensions simultaneously, the system creates multiple simplified views that can be independently analyzed and combined. This segmentation allows users to explore patterns from different perspectives without being overwhelmed by the full complexity of the high-dimensional space.
Data Source
AI summary
A platform, device and process is provided for visual construction of operations for data querying. In particular, embodiments described herein provides a platform, device and process for visual construction of nested operations for data querying. The visual construction is a display of one or more projected data spaces enabling a selection of data indicators on the display. The selection is conducted graphically on the visual construction and the system is configured to translate the selection to generate and conduct a query operating visually on the visualized (e.g., projected) data space. The visual data space includes distinct views of the plurality of multi-dimensionality data points mapped to reduced-dimensionality data points with a transformation function associated with each view. The selections are used to augment the multi-dimensionality data points with one or more additional dimensions to track the selections and to perform operations and visualizations.