# Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review
## 3 Method
### Data Extraction  
To analyse the collected works, we use the categories defined in Section 2, exploring the three spaces involved in the data visualisation pipeline (Data, Visual and Interaction Spaces), as well as chatbot characteristics (the interface, the Input and the complementary Output). In the following, we summarise the categories detailing the tables where the reviewed works are analysed.  
Categories related to the Data Space (see Table 1) include:  
* Description of data.
* Data type (Tabular or Complex).
* Attributes (Nominal, Numerical, Temporal, Spatial).
* Data transformation.  
In relation to the Visual Space (see Table 2), the characteristics considered include:
* Visualisation Category (Basic or Advanced) and Type.
* Abstract (Lines, Points, Bar) and Symbolic (glyphs, icons) graphical elements.
* Visual Mapping Identification (Fixed, User-defined, Rule-based, Intelligent).
* View Transformation (Single or Multiple).
* Interaction Style (Basic--WIMP or Advanced--NL).  
In the Interaction Space, we collect information about the seven interaction methods proposed by [PERSON] et al. [10]: select, explore, reconfigure, encode, abstract/elaborate, filter and connect.  
Finally, regarding chatbots:  
* V-NLI Interface (chatbot-based or form-based).  
* Query Type (low or high);
* One-turn or Follow-up queries;
* Conversational Guidance (Help, Auto-complete, Recommendation);
* Multimodality (WIMP, Touch, Gestures).  
* Feedback (textual or visual): inform, justify, decisions, additional explanations (text, oral, graph, statistics, annotations), UI changes (menus, buttons) and narratives.
* Interaction Style (WIMP, VR, AR).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**V-NLI** & **Year** & **Data Space** & & \\ \hline \hline  & & **Description of Data** & **Data Type** & **Attributes** & **Data Trans.** \\ \hline ACUI [80] & 2017 & Software bundles & Complex (Network) & Nom & \\ \hline Ava [81] & 2020 & Data science & Tabular & Num & ✓ \\ \hline Boomerang [82] & 2021 & Finance & Tabular & Nom, Num & ✓ \\ \hline Chat2 Vis [83] & 2023 & Movies, Cars, etc. & Tabular & Nom, Num & \\ \hline ConVisQA [69] & 2020 & Conversations & Complex (Hierarchical) & Conversation & \\ \hline Data@Hand [84] & 2021 & Health metrics & Complex (Temporal) & Num, Temp & ✓ \\ \hline DataBrezee [85] & 2020 & Colleges & Tabular & Nom, Num & \\ \hline Evizeon [39] & 2017 & Diseases, houses & Tabular & Nom, Num, Spat, Temp & ✓ \\ \hline FlowNL [70] & 2022 & Hurricanes & Complex (Flow) & Hurricanes, Spat, Num & \\ \hline GameBot [86] & 2020 & Sports data & Tabular & Nom, Num, Temp & \\ \hline GeCoAgent [87] & 2021 & Diseases & Tabular & Nom & ✓ \\ \hline InChorus [88] & 2020 & Finance & Tabular & Nom, Num, Temp & ✓ \\ \hline Iris [89] & 2018 & Data science & Tabular & Nom, Num & ✓ \\ \hline MIVA [90] & 2020 & Coronavirus data & Tabular & Nom, Num, Spat, Temp & \\ \hline ONYX [91] & 2022 & Coronavirus data & Tabular & Nom, Num, Spat, Temp & \\ \hline Orko [15] & 2017 & Football players & Complex (Network) & Nom, Num & \\ \hline Snowy [40] & 2021 & Movies & Tabular & Nom, Num, Temp & ✓ \\ \hline Talk2 Data [64] & 2021 & Finance, Cars & Tabular & Nom, Num, Temp & ✓ \\ \hline TransVis [63] & 2021 & Transient & Complex (Network) & Nom, Num & \\ \hline Valetto [92] & 2018 & Cars & Tabular & Nom, Num & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of V-NLIs (ordered alphabetically by name) in defined data categories. Description of data, Data type (tabular and complex), Attributes (Nominal (Nom), Numerical (Num), Temporal (Temp), and Spatial (Spat)), and Data Transformation.