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Datasets encountered in scientific and engineering applications appear incomplex formats (e.g., images, multivariate time series, molecules, video, textstrings, networks). Graph theory provides a unifying framework to model suchdatasets and enables the use of powerful tools that can help analyze,visualize, and extract value from data. In this work, we present PlasmoData.jl,an open-source, Julia framework that uses concepts of graph theory tofacilitate the modeling and analysis of complex datasets. The core of ourframework is a general data modeling abstraction, which we call a DataGraph. Weshow how the abstraction and software implementation can be used to representdiverse data objects as graphs and to enable the use of tools from topology,graph theory, and machine learning (e.g., graph neural networks) to conduct avariety of tasks. We illustrate the versatility of the framework by using realdatasets: i) an image classification problem using topological data analysis toextract features from the graph model to train machine learning models; ii) adisease outbreak problem where we model multivariate time series as graphs todetect abnormal events; and iii) a technology pathway analysis problem where wehighlight how we can use graphs to navigate connectivity. Our discussion alsohighlights how PlasmoData.jl leverages native Julia capabilities to enablecompact syntax, scalable computations, and interfaces with diverse packages.

PlasmoData.jl -- A Julia Framework for Modeling and Analyzing Complex Data as Graphs