Understanding visual analysis processes from user interactions using visual analytics
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Understanding the visual analysis process taken by people when using a visualization application can help its designers improve the application. This goal is typically achieved by observing usage sessions. Unfortunately, many visualization applications are now deployed online so their use is occurring remotely. These remote usages make it very difficult for designers to directly observe usage sessions in person. A solution to the problem is to analyze interaction logs. While interaction logs are easy to collect remotely and at scale, they can be difficult to analyze because they require an analyst to make many difficult decisions about event organization and pattern discovery. For example, which events are irrelevant to the analysis and should be removed? Which events should be grouped because they are related to the same feature? Which events lead to meaningful patterns that help to understand user behaviors? An analyst needs to be able to make these decisions to identify different types of patterns and insights based on an analysis goal. If the analysis goal changes during the process, these decisions need to be revisited in order to obtain the best analysis results. Because of the subjective nature of the analysis process and such decisions, flexibility is required so the process cannot be fully automated. Every decision requires additional effort from an analyst that could reduce the practicality of the analysis process. Therefore, an effective interaction analysis method needs to balance the tradeoffs of flexibility and practicality to best support analysts. Visual analytics provides a promising solution to this problem because it leverages human’s broadband visual analysis abilities with the support of computational methods. For flexibility, the interactive visualizations can ensure an analyst can dynamically adjust decisions in every step of the process to maximize the variety of patterns that could be identified. For practicality, visualizations can help speed up the data inspection and decision-making process while computational methods can reduce the labor in efficiently extracting potentially useful patterns. Therefore, in this thesis I employ visual analytics in a visual interaction analysis framework to achieve flexibility and practicality in the visual analysis process for identifying patterns in interaction logs. I evaluate the framework by applying it to multiple visualization applications to assess the effectiveness of the analysis process and the usefulness of the patterns discovered.