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Interactive visualization is an essential tool for data analysis. Cloud-based data analysis software must handle growing data sets without relying on powerful end-user hardware. This thesis explores and tests various methods to speed up primarily time series plots of large data sets on the web for the biotechnology research company Sartorius.

To increase rendering speed, I focused on two main approaches: downsampling and hardware acceleration. To find which sampling algorithms suit Sartorius's needs, I implemented multiple alternatives and compared them quantitatively and qualitatively. The results show that downsampling increases or eliminates data set size limits and that test users favored algorithms maintaining local outliers. With hardware acceleration that substantially increased the amount of simultaneously rendered points for more detailed representations, these methods pave the way for efficient visualization of large data sets on the web.