  We validate the use of matrix factorization for the automatic identification
of relevant components from atomic pair distribution function (PDF) data. We
also present a newly developed software infrastructure for analyzing the PDF
data arriving in streaming manner. We then apply two matrix factorization
techniques, Principal Component Analysis (PCA) and Non-negative Matrix
Factorization (NMF), to study simulated and experiment datasets in the context
of in situ experiment.
