Rectangular Sets Visualisation

Links: GitHub

About the Project

This project implements rectangular Euler-like sets visualization described in the following paper and its supplementary:

• Paper
• Supplementary

Demo

Data Samples

Additional data samples can be found under the samples folder.

High-level Design

Additional Performance Optimizations

• We use hash tables to minimize duplications when computing intersections, exclusions, or caching constraints. To make sure constraints only appear once, we use sorted hashes.
• Label overlapping will likely occur on intersections, therefore we only compute label constraints for intersecting sets
• Exclusion constraints are only computed for parent-most sets to avoid creating unnecessary constraints, as group exclusion from the parent set automatically means exclusion from all child nodes

Performance Evaluation

Although the project uses a highly efficient LP Solver HiGHS, it cannot achieve the same performance levels as commercial products.

Performance of the application highly depends on the complexity of the dataset. Optimization may be taking from seconds up to tens of minutes for highly intersecting sets.

We therefore introduce constraint toggles, to let the user decide which constraints to compute. This allows the user to trade-off between performance and accuracy.

Feature Overview

Home Screen

Preview Screen

Solution Screen

Setting Up Dev Environment

1. For simplicity, use nvm to manage node versions. Recommended version is >= 18
2. Install yarn package manager: npm install -g yarn
3. Install project dependencies: yarn install
4. Run the project: yarn start

Packaging the Project

To create project binaries simply run:

yarn package

You will find the binaries under the build folder.

Adding new HiGHS binaries

At the moment the project offers two HiGHS binaries (can be found under src/resources/binaries):

1. highs_darwin_arm64 for Apple Silicon (macOS)
2. highs.wasm - C++-based web assembly binary which is used as a fallback option

Building a new HiGHS binary

Note you will need cmake and make for the following steps

The actual platform-specific binary may be a little bit faster. You can therefore build and add a binary by yourself:

make build-highs

The binary (you will find it under .tmp/HiGHS/build) has to be placed under src/resources/binaries and added to the mapping in src/models/solvers/highs.ts.

Testing and Developing components in isolation

yarn storybook

Exporting to HTML

yarn html