Year: 2020
Parallel Mapper
In this paper, we study the parallel analysis of the construction of Mapper. We give a provably correct parallel algorithm to execute Mapper on a multiple processors. Our algorithm relies on a divide and conquer strategy for the codomain cover which gets pulled back to the domain cover.
Continue readingAn Efficient Data Retrieval Parallel Reeb Graph Algorithm
We propose a parallel augmented Reeb graph algorithm on triangulated meshes with and without a boundary. That is, in addition to our parallel algorithm for computing a Reeb graph, we describe a method for extracting the original manifold data from the Reeb graph structure.
Continue readingTopoinvis TTK Hackathon: Experiences, Lessons Learned, and Perspectives
Modeling The Influence Of Visual Density On Cluster Perception In Scatterplots Using Topology
We present a multi-stage user study focusing on 4 factors—distribution size of clusters, number of points, size ofpoints, and opacity of points—that influence cluster identification in scatterplots. From these parameters, we have constructed 2 models, a distance-based model, and a density-based model, using the merge tree data structure from Topological Data Analysis. Our analysis demonstrates that these factors play an important role in the number of clusters perceived, and it verifies that the distance-based and density-based models can reasonably estimate the number of clusters a user observes.
Continue readingLinesmooth: An Analytical Framework For Evaluating The Effectiveness Of Smoothing Techniques On Line Charts
Topolines: Topological Smoothing For Line Charts
We present TopoLines, a method for smoothing line charts using techniques from Topological Data Analysis. The design goal of TopoLines is to maintain prominent peaks in the data while minimizing any residual error. We evaluate TopoLines for 2 visual analytics tasks by comparing to 5 popular line smoothing methods with data from 4 application domains.
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