Creating a Portable Package for Visualizing Categorical Time-series Data
Location
Room 232, Schewel Hall
Access Type
Campus Access Only
Start Date
4-17-2024 10:15 AM
End Date
4-17-2024 10:30 AM
College
Lynchburg College of Arts and Sciences
Department
Computer Science
Keywords
graph, categorical data, histogram, mass evolution, ScottPlot, series, n-gram, time-series, Windows Forms, C#
Abstract
Many tools for visualizing large datasets require the use of specific hardware and software. The aim for this project is to create software for visualizing large datasets without having to worry about specific hardware. We document our work building a portable package for visualizing large categorical time-series data sets. The package is built in C# utilizing the ScottPlot library on the Windows Forms framework. We have implemented a number of visualizations in this tool and will demonstrate our prototype system. The visualizations are created by processing files into a “series” that is used to generate the proper data which can then be fed to a number of graphs to draw different types of visualizations. The tool contains graphs that display total instance counts of characters in a dataset, frequency counts of characters in a dataset, and comparisons of character sequences between parts of a dataset.
Faculty Mentor(s)
Dr. Randy Ribler
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Creating a Portable Package for Visualizing Categorical Time-series Data
Room 232, Schewel Hall
Many tools for visualizing large datasets require the use of specific hardware and software. The aim for this project is to create software for visualizing large datasets without having to worry about specific hardware. We document our work building a portable package for visualizing large categorical time-series data sets. The package is built in C# utilizing the ScottPlot library on the Windows Forms framework. We have implemented a number of visualizations in this tool and will demonstrate our prototype system. The visualizations are created by processing files into a “series” that is used to generate the proper data which can then be fed to a number of graphs to draw different types of visualizations. The tool contains graphs that display total instance counts of characters in a dataset, frequency counts of characters in a dataset, and comparisons of character sequences between parts of a dataset.