Third International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV 2005)

CMV 2005 is the third conference on Coordinated and Multiple Views in Exploratory Visualization. The aim of this conference is to bring together top researchers in the area, to stimulate discussion and debate ideas and future opportunities. This conference focuses on all aspects of multiple-view techniques including: multiform views, tight coupling, linked dynamic interaction, multiple views for interactive steering, and spreadsheet based visualisation techniques. The emphasis is on generating and manipulating multiple views and coordinating information between them. It is clear why users wish to explore, interact and interrogate their data. They are often faced with unknown data of huge proportions, with many variables and multiple dimensions. As such they are faced with an insurmountable comprehension task. Multiple views that are coordinated together can enhance and aid the user in this process. The first CMV conference was held in 2003. Over the last few years this field has developed and grown. In fact, many of the papers presented this year detail how multiple linked views can be used to solve a particular problem in a particular application domain. This is certainly an exciting time as we see CMV techniques and principles applied to solve real problems. But, there is still much work to be done. Not only discovering how to appropriately apply these ideas to new application domains, but to discover more effective CMV techniques and designs. One aspect of this research is the study and use of 'sharing things' for exploratory visualisation. Taking this simplistic view, we may learn from concepts such as sharing hardware devices in a computer system or managing, delegating roles in a human organization or collaborative support. During last year's conference we had an open discussion about the status of the area; it was generally agreed that we were reasonable at some aspects, such as manipulation (zoom, brush, etc.) and simultaneous exploration, but needed more effort into challenges such as: validation and evaluation of the effectiveness and appropriateness of the technique (e.g., whether two or more views are best, when one technique should be used over another), measuring quantities (especially measuring and comparing quantities over multiple windows), managing the whole visualization exploration, and integrating temporal data. It is encouraging that some of these challenges are being addressed in the papers of this volume. Indeed, this volume presents the current state of the art in CMV and allows the reader to discover more about this exciting and developing area.