# Datamator: An Authoring Tool for Creating Datamations via Data Query Decomposition
## 7 Evaluation
### Interview with Experts  
Two expert users were interviewed separately. The first expert (\(E1\)) was a researcher in data visualization who had published over 10 papers in _IEEE Transactions on Visualization and Computer Graphics_ (TVCG). The second expert worked for an IT consulting company, and their job was to analyze customer data using tools like PowerBI and Tableau. During the interview, the experts were asked to create datamations using Datamator based on datasets selected from the Spider corpus. They tried a number of data queries and the corresponding datamations were created. Their operations, generation results, and comments were recorded in detail. Each interview lasted for about 1.5 h.  
**Interview Feedback** Due to page limitations, we restrict our report to summarizing only some major feedback:  
_Data query decomposition._ All the experts felt that starting a datamation generation process by inputting a natural language query was a \"good idea that greatly reduces the technique barriers\". \(E1\) mentioned that to\"resolve a natural language question into data operations is useful for users with little data analysis background\". \(E2\) said that \"although [the system] cannot always provide a comprehensive [decomposition] result, it is always a good start by pointing out the potential directions to solve problem\".  
_The authoring tool._ All experts were able to successfully generate datamations based on our system. They were quite excited when seeing an analysis pipeline shown in an animation. \"This is my first time to see such kind of tool\", said \(E2\) and he continued \"this feature is very nice, I can hardly generate this kind of animations using the tools that I have ever used\". \(E1\) believed that \"showing the analysis via an animation is very intuitive\" and \"it is a good strategy for interpreting an analysis result\". At the same time, both \(E1\) and \(E2\) praised the interactive authoring functionalities supported in our system and even constructively suggested a number of new features such as \"recommend proper operations for users to choose when creating an analysis pipeline (\(E2\))\" and \"provide more visualization styles (\(E1\))\". In addition, \(E1\) believed that our feedback mechanism is useful as \"it could help the underlying model to reduce errors in the next time\". \(E2\) also mentioned that \"[with the help of feedback] I can make the result better and better\". Two datamations authored by experts are shown in Figure 7.  
**Discussion** The feedback from experts indicates that they found our datamation compelling for conveying analysis pipelines. The interactive authoring features were well received, with experts suggesting enhancements like guided operations and varied visualization styles. The feedback mechanism was acknowledged as valuable for the iterative improvement of results. Overall, the experts' responses affirm the system's effectiveness in intuitive datamation authoring, underscoring its potential for practice.  
Figure 7: Datamations authored by our expert users based on a dataset consisting of 389 cars.