Abstract
Grouping and un-grouping of data are considered effective techniques to manipulate huge amount of data. While conducting analysis and visualization of data, there is much difficulty in tracing the interaction not only between the groups of data but also among the group members. Grouping can be done using composites. In this paper, we have conducted a research review regarding grouping of data and composites. We have described composites from different angles. In doing so, we have studied variety of challenges confronting the composites. To address those challenges, we have proposed some refined composite network visualization features in our framework for visualization of networks, PEVNET. With the aid of these features, the analysts can drag and drop data for effective decision making. We have introduced three ways of grouping individual and composite data which include grouping the selected nodes, merging node into group, and afterwards un-group it. In our previous work, we have implemented merging group into group. We have also make the job of the analyst easier by retrieving the details of each group member. We believe that by using the proposed composite network visualization in PEVNET, the analysis and visualization of network data will become more effective.
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