Web Science and Digital Libraries Research Group

(Previous semester Information Visualization highlights posts: Fall 2017 , Spring 2017 , Spring 2016 , Spring 2015 , Spring/Fall 2013 , Fall 2012 , Fall 2011 ) In Fall 2019, I introduced CS 625: Data Visualization , a new graduate-level visualization course. (This course was taught in a flipped+hybrid manner, as I described in an earlier blog post .) We used the same textbook, Tamara Munzner's Visualization Analysis and Design , as in my previous CS 725/825 Information Visualization courses, but this course was designed to be a gentler introduction to visualization and data analysis. We focused on basic visualization design principles and on how to ask good questions rather than D3 programming. Students were allowed to use whatever tool they wished, but I emphasized clear design no matter what tool was used. Over the course of two assignments ( HW7 , HW8 ), students developed questions about real-world data, developed a draft visualization, and then refined the visualization based

This image by NASA is licensed under NASA's Media Usage Guidelines In Part 1 , we introduced Hypercane , a tool for automatically sampling mementos from web archive collections. Web archive collections consist of thousands of documents, and humans need tools to intelligently select mementos for a given purpose. Hypercane's goal is to supply us with a list of memento URI-Ms derived from the input we provide. In Part 2 , I highlighted how Hypercane's synthesize action converts its input into other formats like JSON for Raintale stories, WARCs for Archives Unleashed Toolkit , or boilerplate-free files for Gensim . This post focuses on the primitive advanced actions that make up Hypercane's sampling algorithms. We can mix and match different primitives to arrive at the sample that best meets our needs. The DSA project 's goal is to summarize a web archive collection by selecting a small number of exemplars and then visualize them with social media stor

This image by NOAA is licensed under NOAA's Image Licensing & Usage Info . In Part 1 of this series of blog posts, I introduced Hypercane , a tool for automatically sampling mementos from web archive collections. If a human wishes to create a sample of documents from a web archive collection, they are confronted with thousands of documents from which to choose. Most collections contain insufficient metadata for making decisions. Hypercane's focus is to supply us with a list of memento URI-Ms derived from the input we provide. One of the uses for this sampling is summarization. The previous blog post in this series focused on its high level sample and report actions and how they can be used for storytelling. This post focuses on how to generate output for other tools via Hypercane's synthesize action. The goal of the DSA project : to summarize a web archive collection by selecting a small number of exemplars and then visualize them with social media