Scatterplot Encoding Using Resolution-Based Character Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern chart creation software requires large datasets to be in text form, such as JSON or XML, which can lead to significant memory usage and network bandwidth requirements due to the representation of numbers, causing performance issues with large datasets like scatterplots.
Innovation Solution
A method for compressing scatterplot data by determining a resolution for each dimension, partitioning the data frame into grids, and encoding each data point using a predetermined resolution mapping and character encoding, reducing the number of characters required to represent the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If scatterplot data is represented in text form (JSON/XML), then the data can be processed by chart creation software, but memory usage and network bandwidth requirements increase significantly
Solution Approach 1:
The patent changes the parameter of data representation from full-precision text format to a compressed format using resolution mappings and character encoding. Each data point is encoded using a limited set of characters based on predetermined resolutions for each dimension, dramatically reducing the amount of data while maintaining visual integrity when rendered at the specified resolution.
Solution Approach 2:
The patent extracts only the essential information needed for rendering scatterplots at a given resolution. Instead of storing complete high-precision numerical values, it extracts and stores only the discrete character codes that map to specific data point positions, eliminating redundant precision information that would not be visually distinguishable.
2Measurement precision
If high-precision numbers are used in scatterplot datasets, then measurement precision is improved, but data size and processing time increase
Solution Approach 1:
The patent changes the precision parameter from high-precision floating-point numbers to discrete character codes. The resolution mappings define the precision level needed for visual representation, and data points are encoded using these mappings, reducing processing time while maintaining sufficient precision for the intended display resolution.
3Quantity of substance
If large datasets are compressed using existing methods, then data size is reduced, but compression ratios are insufficient for scatterplot data
Solution Approach 1:
The patent applies parameter changes by transforming continuous numerical data into discrete character representations using resolution-based mappings. This transformation exploits the fact that scatterplots are rendered at specific resolutions, allowing aggressive compression while preserving visual quality, achieving superior compression ratios compared to generic compression methods.
Solution Approach 2:
The patent segments the data space into discrete bins defined by the resolution mappings for each dimension. Each segment corresponds to a character code, and multiple data points falling within the same bin are represented by the same character, enabling efficient compression of datasets with many points at similar positions.
Data Source
AI summary
Provided is a method for encoding scatterplot data using strings. The method may comprise receiving a plurality of data points in a data set. Each data point has at least a first data value corresponding to a first dimension and a second data value corresponding to a second dimension. The method further comprises determining a first resolution for the first dimension and a second resolution for the second dimension. The method further comprises determining an encoding scheme for encoding the plurality of data points. The encoding scheme includes a plurality of valid encoding characters. The method further comprises encoding each of the plurality of data points based on the first resolution, the second resolution, and the encoding scheme.