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

Abstract

Visual analysis dialogue system utilizing natural language interface is emerging as a promising data analysis tool. However, previous work mostly focused on accurately understanding the query intent 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 incomplete user query (i.e., input query can not reflect all possible intentions of the user). A query recommendation algorithm, which applies the historical query information of a user to recommend follow-up query, is also designed and implemented to provide in-depth exploration. These ideas are implemented in a system called DT2VIS. Specific cases of utilizing DT2VIS are also provided to analyze data. Finally, results show that DT2VIS could help users easily and efficiently reach their analysis goal in a comparative study.