Abstract
Grouping of data is recognized as an effective way of managing a huge amount of data. Groups are very important for exploratory analysis of visualized networks. There are different issues with grouping; for instance data gets meshed up together which makes the interaction between the group members difficult to trace, the analysts find it difficult to analyze the data properly, and thus visualizing data for finding patterns become complex. We have studied different techniques for visualization of criminal data and found that by using different features of composites, the interaction between the different sub-groups can be improved to a large extent. In our proposed framework for visualization of networks, PEVNET, we have made an implementation with which the analysts can drag and drop data for efficient manipulation and have introduced two novel ways of grouping individual and composite data which include grouping the selected nodes and merging group into another group. Finally un-grouping groups is performed. We hope that by including these features, the PEVNET will serve as a handy tool for the analysts, since each and every feature of PEVNET is fulfilling most of the requirements that are needed to conduct a comprehensive analysis.
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Rasheed, A., Wiil, U.K., Abdullah, A. (2018). Composite Visualization Features in PEVNET: A Framework for Visualization of Criminal Networks. In: Moshirpour, M., Far, B., Alhajj, R. (eds) Highlighting the Importance of Big Data Management and Analysis for Various Applications. Studies in Big Data, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-60255-4_3
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DOI: https://doi.org/10.1007/978-3-319-60255-4_3
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