Time plays a huge role in our lives. We use our understanding of time to determine when an event occurs, to place events in sequence after one another, and to compare how long events last. An event is defined as something that takes place in time; and each event has a duration associated with it, and occurs between different entities. For example, bank transactions across accounts, air traffic across airports etc. Events tend to accumulate over time, and examining them becomes rather tedious. Creating a visualization of these temporal events makes it easier to understand the patterns of interaction that these events weave over time.

In this thesis, an approach to visualizing such dense temporal events is investigated. When events are densely packed together, the resulting issues are highlighted and techniques to alleviate them are presented. The visualization developed enables uncovering interesting patterns hidden in such dense temporal data. The pros and cons of using splines versus straight lines for representing dense temporal events are discussed at length. Different data- sets like software traces data-set, an earthquake data-set, a coral reef disease data-set and a ferry data-set are utilized in order to test the performance of straight lines against splines.