Real-time event based visualization of multivariate abstract datasets : Implementing and evaluating a dashboard visualization prototype

Abstract: As datasets in general grow in size and complexity over time while the human cognitive ability to interpret said datasets essentially stays the same, it becomes important to enable intuitive visualization methods for analysis. Based on previous research in the field of information visualization and visual analytics, a dashboard visualization prototype handling real-time event based traffic was implemented and evaluated. The real-time data is collected by a script and sent to a self-implemented web server that opens up a websocket connection with the dashboard client where the data is then visualized. Said data consisted of transactions and related metadata of an ecommerce retail site applied to a real customer scenario. The dashboard was developed using an agile method, continuously involving the thesis supervisor in the design and functionality process. The final design also depended on the results of an interview with a representative from one of the two target groups. The two target groups consisted of 5 novice and 5 expert users to the field of information visualization and visual analytics. The intuitiveness of the dashboard visualization prototype was evaluated by conducting two user studies, one for each target group, where the test subjects were asked to interact with the dashboard visualization, answer some questions and lastly solving a predefined set of tasks. The time spent solving said tasks, the amount of serious misinterpretations and the number of wrong answers was recorded and evaluated. The results from the user study showed that the use of colors, icons, level on animation, the choice of visualization method and level of interaction were the most important aspects for carrying out an efficient analytical process according to the test subjects. The test subjects desired to zoom in on each component, to filter the contents of the dashboard and to get additional information about the components on-demand. The most important result produced from developing the dashboard was how to handle the scalability of the application. It is highly important that the websocket connection remain stable when scaling out to handle more concurrent HTTP requests. The research also conclude that the dashboard should handle visualization methods that are intuitive for all users, that the real-time data needs to be put into relation to historical data if one wishes to carry out a valid analytical process and that real-time data can be used to discover trends and patterns in an early-as-possible stage. Lastly, the research provides a set of guidelines for scalability, modularity, intuitiveness and relations between datasets.