Amer Rasheed

Novel Visualization Features of Temporal Data Using PEVNET

The information visualization of networks has been a tricky task during the last decade. It is difficult to understand such large amounts of statistical data. A number of solutions have been proposed to tackle this bulk of information. By examining some dynamics of criminal networks and by making use of some novel interactive features, we have found that the prevailing challenges to information visualization can be eliminated to a large extent. The current study will help understand interesting patterns, which are extracted by way of monitoring the temporal data of a criminal activity. We have appended six more features to the PEVNET framework. These are ‘Node color feature’, ‘Link size feature’, ‘Link details on demand feature’, ‘Detecting collaborating sub-cluster feature’, ‘Sub-cluster detection feature’, and ‘Temporal pattern feature’. A novel clustering algorithm has been proposed. We have proposed a unique way of visualizing the clustering of data, with which the analyst gets a sound visualization of the data.

Evaluating PEVNET: A Framework for Visualization of Criminal Networks

Information visualization has been a burning topic among the researchers in the recent decade. Getting targeted information, which is everyone’s desire, is becoming difficult with the abundance of data. In this research, we have made an evaluation of our proposed framework PEVNET by conducting an experiment. Thirty two participants evaluated the system. The experiment was performed in two phases. In the first phase, a usability evaluation and qualitative feedback was carried out to check whether the PEVNET framework provided adequate results to the users. The qualitative feedback was performed by considering two aspects: the ease of use and the functionality. In the second phase, the comparison of the PEVNET had been performed against another state-of-the-art tool. Locating the central person, detecting the hidden interaction patterns between the sub-clusters, and detecting temporal activity were among the main tasks that were to be achieved by the participants. These tasks were to be performed in the groups of participants. The case study of Chicago Narcotics datasets was used. We found that the participants, of the PEVNET group, performed the tasks faster as compared to the other techniques used in the experiment. Among the participants, there were a few domain experts who appreciated our novel visualization features. Anecdotally, we believe that by evaluating the PEVNET in this research paper, we will be able to get the confidence of the crime analysts. We have found that the network visualization of the PEVNET framework, based on the experimental results, has gotten satisfactory feedback from the majority of the participants.

Visualizing Composites in PEVNET: A Framework for Visualization of Criminal Networks

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.

Composite Visualization Features in PEVNET: A Framework for Visualization of Criminal Networks

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.

A Tool for Analysis and Visualization of Criminal Networks

Analyzing complexities in criminal networks is a complex issue. They become even worse when there is involvement of external collaborative networks. Criminal nodes in different criminal sub-groups combine together to form a big network. It is difficult to explore criminal activity that is building up among the sub-groups. Data from the initial investigations reveal only partial information. Hence, there is a need to find links between the data for getting adequate information. We have introduced novel visualization features that can help trace the collaborations of the individual criminal nodes with other nodes and detect the patterns of hidden criminal activities in the sub-clusters. The current study demonstrates our proposed visualization tool by using a case study of the Chicago narcotics datasets. The PEVNET tool can support crime analysts in analyzing the intra-network criminal activities. Our novel features will not only help the crime analysts in building a rationale but also in strengthening their viewpoints using PEVNET.