Parallel Coordinates Version of Time-Tunnel (PCTT) and Its Combinatorial Use for Macro to Micro Level Visual Analytics of Multidimensional Data

This chapter treats an interactive visual analysis tool called PCTT, Parallel Coordinates Version of Time-tunnel, for multidimensional data and multi-attributes data. Especially, in this chapter, the author introduces the combinatorial use of PCTT and 2Dto2D visualization functionality for visual analytics of network data. 2Dto2D visualization functionality displays multiple lines those represent four-dimensional (four attributes) data drawn from one (2D, two attributes) plane to the other (2D, two attributes) plane in a 3D space. Network attacks like the intrusion have a certain access pattern strongly related to the four attributes of IP packet data, i.e., source IP, destination IP, source Port, and destination Port. So, 2Dto2D visualization is useful for detecting such access patterns. Although it is possible to investigate access patterns of network attacks at the attributes level of IP packets using 2Dto2D visualization functionality, statistical analysis is also necessary to find out suspicious periods of time that seem to be attacked. This is regarded as the macro level visual analytics and the former is regarded as the micro level visual analytics. In this chapter, the author also introduces such combinatorial use of PCTT for macro level to micro level visual analytics of network data as an example of multidimensional data. Furthermore, the author introduces other visual analytics example about sensor data to clarify the usefulness of PCTT.