Although the discovery and analysis of communication patterns in large and complex email datasets are difficult tasks,
they can be a valuable source of information. We present EmailTime, a visual analysis tool of email correspondence
patterns over the course of time that interactively portrays personal and interpersonal networks using the correspondence
in the email dataset. Our approach is to put time as a primary variable of interest, and plot emails along a time line.
EmailTime helps email dataset explorers interpret archived messages by providing zooming, panning, filtering and
highlighting etc. To support analysis, it also measures and visualizes histograms, graph centrality and frequency on the
communication graph that can be induced from the email collection. This paper describes EmailTime's capabilities,
along with a large case study with Enron email dataset to explore the behaviors of email users within different
organizational positions from January 2000 to December 2001. We defined email behavior as the email activity level of
people regarding a series of measured metrics e.g. sent and received emails, numbers of email addresses, etc. These
metrics were calculated through EmailTime. Results showed specific patterns in the use email within different
organizational positions. We suggest that integrating both statistics and visualizations in order to display information
about the email datasets may simplify its evaluation.
In this paper we introduce Musician Map, a web-based interactive tool for visualizing relationships among popular musicians who have released recordings since 1950. Musician Map accepts search terms from the user, and in turn uses these terms to retrieve data from <i>MusicBrainz.org</i> and <i>AudioScrobbler.net</i>, and visualizes the results. Musician Map visualizes relationships of various kinds between music groups and individual musicians, such as band membership, musical collaborations, and linkage to other artists that are generally regarded as being similar in musical style. These
relationships are plotted between artists using a new timeline-based visualization where a node in a traditional node-link diagram has been transformed into a Timeline-Node, which allows the visualization of an evolving entity over time, such as the membership in a band. This allows the user to pursue social trend queries such as "Do Hip-Hop artists collaborate differently than Rock artists".