Studying the evolution of long lived processes such as the development history of a software system or the publication history of a research community, requires the analysis of a vast amount of data. Aggregation techniques and data specific techniques are usually used to cope with the large amount of data.

In this paper, we introduce a general technique to study historical data derived from tracking the evolution of long lived processes. We present a visualization approach (evolution spectrographs) to assist in identifying interesting patterns and events during evolutionary analysis of such historical data. We demonstrate the usefulness of spectrographs through several case studies. The data for the case studies are derived from the publication history of conferences in the area of software engineering and from the source control of several large open source projects. Our case studies reveal interesting patterns such as the increase of collaboration over time in the area of software engineering, and the emergence of new research topics. The spectrographs give an overview of the change activities for the subsystems in large software projects.