DT2VIS: A Focus plus Context Answer Generation System to Facilitate Visual Exploration of Tabular Data

The visual analysis dialog system utilizing natural language interface is emerging as a promising data analysis tool. However, previous work mostly focused on accurately understanding the query intention of a user but not on generating answers and inducing explorations. A focus+context answer generation approach, which allows users to obtain insight and contextual information simultaneously, is proposed in this work to address the incomplete user query (i.e., input query cannot reflect all possible intentions of the user). A query recommendation algorithm, which applies the historical query information of a user to recommend a follow-up query, is also designed and implemented to provide an in-depth exploration. These ideas are implemented in a system called DT2VIS. Specific cases of utilizing DT2VIS are also provided to analyze data. Finally, the results show that DT2VIS could help users easily and efficiently reach their analysis goals in a comparative study.

Automated summary

The study introduces a focus+context answer generation approach and a query recommendation algorithm to address incomplete user queries and provide in-depth exploration and contextual information. Using the system DT2VIS can help users more easily achieve their analysis goals.