Detecting Abrupt Changes and Emerging Trends

In this chapter, we started with a number of examples outside the mainstream information visualization to illustrate the complexity of detecting abrupt changes in phenomena such as the collapse of a civilization, detecting thematic changes, monitoring business activities in recessions, and identifying intellectual turning points. The second part of the chapter introduces the CiteSpace system and its application to the visualization of superstring revolutions.We have emphasized the significant role of knowledge discovery and data mining techniques in the second generation of information visualization. The success of the first generation of information visualization is largely related to the structure- centric focus. We argue that the second generation of information visualization needs to go beyond the structure-centric mindset. One of the crucial components of the second generation is a dynamics-centric focus, which is an emphasis of increasing challenges to visualize the profound dynamics that govern rapid as well as gradual changes in so many practical issues. In this context, we also call for more substantial integration between knowledge discovery and data mining and the mainstream of information visualization. As information visualization extends to a wider range of practical domains, it is inevitable to incorporate technologies that can efficiently detect changes and emerging patterns.