# Datamator: An Authoring Tool for Creating Datamations via Data Query Decomposition
## 8 Conclusions, Limitations, and Future Work  
In this paper, we presented Datamator, developed for creating datamations. The quantitative evaluation shows that the decomposition network can understand the users' questions and resolve them in a sequence of steps. Furthermore, the calibration network can precisely fine-tune the parameter of the decomposition network's parameters to produce desired results as suggested in user feedback. Through our user study, we validated the data generated by Datamator and eventually showed that they can effectively convey insights. This validation was underscored by the outcomes of expert interviews, which consistently appraised Datamator's proficiency in generating datamations that elucidate intricate data analysis procedures. The tool's editing functionality also proved to be adept in rectifying machine-generated results, as evidenced by the study's outcomes.  
Still, based on the interview feedback and observations from the study, there are a number of limitations of our work that are worth studying in the future.  
A limitation of the decomposition model stems from the fact that both the decomposition and calibration networks rely on pretrained models. Consequently, these networks exhibit restricted capabilities when confronted with difficult queries and queries that are very different from the training data. This constraint arises due to the finite scope of the training data used to pretrain the networks. One potential solution to address this limitation is to encompass transfer learning or continual fine-tuning on diverse datasets. A robust adaptation mechanism could enable the networks to gradually incorporate knowledge from new data instances, thereby enhancing their adaptability to unseen scenarios.  
Another limitation pertains to the current calibration network's inability to holistically enhance the performance of the decomposition network. This limitation is rooted in the limited interplay between the two networks, preventing synergistic refinements that could optimize the decomposition process based on both input data and user feedback. The design of the calibration network is intended to be a temporary solution used to optimize the performance of the model during the accumulation of new training samples. To improve the overall performance, the development of a dynamic knowledge accumulation mechanism is proposed. Such a mechanism would systematically gather new samples from user feedback and iteratively adapt the decomposition network, culminating in a more refined and effective model.  
The last limitation centers around the scalability of the visualization design, specifically when dealing with multidimensional data [61], time-series data [62], and large amounts of data. To surmount this limitation, interactive visualizations that allow users to zoom, filter, and interact could be included. This way, users could focus on specific parts of the dataset without overwhelming the visualization with excessive data points. It would also be possible to interactively modify the attributes of the data being visualized.  
The following supporting information can be downloaded at: www.mdpi.com/xxx/s1; Video SI: The video abstract of our work and the system demo.  
Conceptualization, Y.G., D.S., N.C. and D.W.; methodology, Y.G., D.S., N.C. and D.W.; software, Y.G., Y.W. and L.C.; validation, Y.G. and L.C.; formal analysis, Y.G. and L.C.; investigation, Y.G.; resources, Y.G.; data curation, Y.G. and L.C.; writing--original draft preparation, Y.G.; writing--review and editing, N.C. and D.W.; visualization, Y.G., Y.W. and L.C.; supervision, Q.C., N.C. and D.W.; project administration, N.C.; funding acquisition, Q.C., N.C. and D.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NSFC 62061136003, NSFC 62002267, NSF Shanghai 23 ZR1464700, NSFC 62072338, and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) 449742818.  
Not applicable.