The hardworking analysts at FishEye International are trying to identify potential instances of illegal fishing by trawling through a sea of data. The task at hand is difficult enough already given the expansive and diverse data they work with. To ease their analysis, FishEye started an initiative to join information from different data sources into a single cohesive knowledge graph. The result of this effort is known as CatchNet: the Oceanus Knowledge Graph. In this Grand Challenge, you will use the full knowledge graph that combines data from all 3 mini-challenges.
Despite early success, FishEye’s leadership has begun to suspect that CatchNet data has been manipulated to hide illegal fishing activities! Your task is to develop visual analytics approaches to address these elements:
Element 3 may require augmenting/editing data. A visual analytics tool to edit the data is not a part of this challenge; use any convenient means to make the corrections. The comparison of before & after is the focus.
In this grand challenge, it is especially important to create visualizations and visual analytics approaches that integrate data from the whole across the different sources in the CatchNet knowledge graph.
Using data from all 3 mini-challenges including the CatchNet knowledge graph and source documents and visual analytics, answer the following questions:
What data was manipulated? Provide visual evidence to support you assertions.
Use your visual analysis to illustrate how biased/incorrect data made its way to into CatchNet. Who or what may have been responsible for introducing bias into FishEye’s data? What are the impacts of different bias sources? Use visualizations to provide evidence for your conclusions.
Make corrections using any available techniques, algorithmic or manual. Using visual techniques to illustrate, how do the original and corrected data differ? Who benefitted from influencing the data? In other words, identify who manipulated the data and provide visual evidence for the illegal fishing activity they may have been trying to hide.
Use visualizations to illustrate how FishEye can improve the reliability of CatchNet. What are the characteristics of data that should treated with more care in the future?
Note: the VAST challenge is focused on visual analytics and graphical figures should be included with your response to each question. Please include a reasonable number of figures for each question (no more than about 6) and keep written responses as brief as possible (around 250 words per question). Participants are encouraged to new visual representations rather than relying on traditional or existing approaches.
Reflection Questions
Which version of the data did you choose to work with and why? Did you download more than one version and change course during the challenge?
Given the task to develop visualizations for knowledge graphs, did you find that the challenge pushed you to develop new techniques for visual representation?
Did you participate in last year’s challenge? If so, did your experience last year help prepare you for this year’s challenge?
What was the most difficult part of working on this year’s data and what could have made it more accessible?
VAST 2024 Submission Instructions
All VAST data is fully synthetic. Any resemblance to real people, places, or events is purely coincidental. Some elements of the 2024 VAST Challenge resemble data released for the 2023 VAST Challenge. Participants should not assume that there is any continuity and should not use any earlier or any external data for their submission. Grand Challenge participants should use all VAST 2024 data. The GC data is simply data from all 3 mini-challenges added together. Participants preparing submissions for any mini-challenge may also prepare a grand challenge, but participation in a mini-challenge is not required.