2. Data Visualisation
A common data visualisation process consists of several steps 
. Figure 1
details the data flow through these steps constructing the visual structures and how the end-user can interact with the data involved in each step (from right to left, see the arrows in the lower part of the figure), filtering regions (View Transformation
), changing visual parameters (Visual Mapping
), and making more complex requests on the data (Data Transformation
). Starting from the three spaces in which the visualisation takes place—Data Space, Visual Space and Interaction Space—researchers present the most relevant characteristics that will serve as a basis for describing the works under study in this scoping review.
Overview of the Data Visualisation pipeline adapted from 
2.1. Data Space
The Data Space
(shown in green in the upper-left part of Figure 1
) covers the space in which the data are directly processed. When the input data are in a tabular format, the Data Transformation
stage usually offers a set of operations to filter, cluster and aggregate data, among other functions, which can help to provide some data insights. Actually, these data categories help to identify the Data Transformation (see the first blue square in Figure 1
), which is decisive for discovering insights in the data. Classical data transformations such as grouping, aggregation, enclosure and binning temporal items are widely associated with specific data categories in the visualisation community 
. For instance, while aggregation functions such as mean and sum are suitable for quantitative data, grouping is better suited to nominal and ordinal data, and binning intervals is the right transformation in the case of temporal samples 
. In addition, recent works have proposed more complex transformations of multidimensional datasets to extract meaningful subsets using relational queries 
. In the case of connected structures, the topology can play an important role in the transformations, and also in the next stage of Visual Mapping 
. For instance, extracting the largest path is a common transformation in elongated trees, and obtaining the widest level is a more typical transformation in compact hierarchies. Therefore, regarding the data types
and their different transformations, researchers categorised data as: (1) tabular data
, i.e., data with individual and non-connected items, where classical data transformations are enough, and (2) complex data
, i.e., high-dimensional, temporal and interconnected data, which require more complex transformations. Moreover, both categories of data not only involve different transformations but also different strategies in the successive steps of the pipeline. Researchers summarise in Figure 2 the main trends working on the Data Space. The figure also details the kind of data attributes each V-NLI supports (nominal, numerical, temporal, spatial).
Figure 2. Data Space overview and the main characteristics of the data involved in the visualisation pipeline.
In general, research works cover a wide range of real-life applications (movies, sports, coronavirus, finance and others). Most of them used multidimensional tabular data  while few of them also included spatial data . Moreover, it seems that the exploration of complex data is still an emerging field in this type of natural language interactions. For instance, Ref.  has data related to software bundles and services such as OSGi bundles, and  uses network data displaying the relationships between football players. Furthermore, the ConVisQa system  has hierarchical data that is collected from online conversations and  works with flow data such as hurricanes. Finally, Ref.  has sequential temporal data (e.g., sleep time during each night), and 
has transient data which is a data type that is relevant to a time period; in this case, it is the quality of software services over time.
2.2 Visual Space
The second space involved in the data visualisation process is the Visual Space (shown in blue in the upper-middle part of Figure 1), which refers to how to map the data in visual structures (the Visual Mapping Step) and how to display them in a viewport (the View Transformation Step).
The Visual Mapping Step involves the definition of the next three aspects:
The spatial substrate—i.e., the space and the layout used to map the data;
The graphical elements—i.e., marks such as points, lines, images, glyphs, lines, etc.;
The graphical properties—also called retinal properties, i.e., size, colour, orientation, etc. 
In the spatial substrate, a wide variety of layouts for displaying data have been proposed, from the simplest, such as those based on coordinate axes, to the more complex, such as those representing networks 
. In fact, the more basic and simple they are, the more they are exploited in different applications. Researchers classify these layouts as basic
. Basic layouts refer to chart-based layouts, which have x and y axes (e.g., bar, line, scatter plot), table-based layouts and map-based layouts (such as a bubble map). Researchers consider advanced layouts to be those that deal with higher dimensionalities (e.g., parallel coordinates) and with connections (e.g., radial tree, circle packing, network graph, sunburst diagram, chord diagram). Most of the articles employed explicitly basic visualisations 
. The most common methods are bar charts, line charts and scatter plots.
Figure 3. View Space overview and the main characteristics of the Visual Mapping and the View Transformation steps.
Researchers consider advanced layouts to be those that deal with higher dimensionalities (e.g., parallel coordinates) and with connections (e.g., radial tree, circle packing, network graph, sunburst diagram, chord diagram). Only a small percentage of these studies used advanced visualisation methods. For example, Bieliauskas and Schreiber  and Orko  implemented network visualisations as their main visualisation. In addition, Orko includes additional basic visualisation methods such as a bar chart to support its main visualisation. On the other hand, Tansvis  uses a line graph as the main visualisation for analysing transient data (quality of the software system over time), though it has a network graph for displaying the overview of the software system, where users can select a part to explore transient behaviours. ConVisQA  created a novel design to show the hierarchical structure of conversations using stacked bar charts with indentations to show the hierarchy. ConVisQA also displays the conversations on the right-hand side of the screen. InChorus  supports popular basic visualisations such as bar, line and scatter, and they also included one complex option, parallel plots. FlowNL  used flow visualisation to show flows occurring on the earth (e.g., hurricanes). FlowNL also included basic visualisation methods for giving additional information such as a bar chart displaying the velocity of the hurricanes.
To analyse the reviewed papers in terms of the visual mapping identification, i.e., to choose layouts and graphical elements and properties, researchers use the following categories: fixed, user-defined, rule-based (researchers refer to basic rule-based methods as those that follow a set of heuristics and make decisions based on them), and intelligent methods (intelligent methods involve the use of machine learning, artificial intelligence or other computational techniques to enable systems to learn from data, adapt and make decisions in a more flexible and adaptive manner). Researchers examined the Visual Mapping Identification in the previous works and found that most of them only have fixed visual mapping . Meanwhile, only few of them support user-defined mapping , while some use the rule-based visual mapping  method. Finally, there are V-NLIs that support a combination of two visual mappings .
Once the visual mapping is performed, the View Transformation
stage allows users to change the viewpoint (e.g., zooming and panning), perform location probes (to measure values in samples), and create some distortions in the image (i.e., change the projection type) 
. Additionally, view transformation allows users to take into account multiple views simultaneously, as well as animations and others. Some view transformations emphasise data with importance-driven strategies to enhance values and regions of interest, among other factors. Focus+Context 
highlights the important data (focus) while the rest of the data provide additional information on the background (context), which allows users to see the details as well as the entire perspective. Researchers
will describe the reviewed works in terms of the number of views that they use simultaneously (Single/Multiple)
and the strategy
used to emphasise regions or parts of the view (zoom, panning, focus+context, level of detail, multiresolution and others). Related to View Transformation, most of the explored works use a single view to visualise data 
. There are nine V-NLIs that have multiple views. Boomerang 
displays multiple recommended visualisations simultaneously, while Talk2Data 
generates multiple visualisations with annotations in a visualisation narrative style. In the case of MIVA 
, there are three fixed visualisations (bar, line, map), which are simultaneously updated to answer users’ queries. Similarly, Evizeon 
supports synchronised multiple views. Moreover, in Data@hand 
and TransVis 
, multiple visualisations can be observed. Chat2Vis 
demonstrates visualisation outputs using three views that use different LLM models to compare their performance. Finally, Orko 
and FlowNL 
have complementary visualisations in addition to primary ones.
2.3 Interaction Space
Last but not least is the Interaction Space
(shown in blue in the upper-right part of Figure 1
), where the users interact with all the previous steps defined above. There have been many attempts to categorise different interactions 
. Yi et al. 
proposed seven interaction methods based on the user’s intents: select
. All those interactions can be done by users throughout the different stages of the visualisation pipeline (see Figure 4). Notice that users can utilise all these methods through different interaction styles. Researchers
consider a coarse two-labelled categorisation: Basic and Advanced. Basic styles refer to the WIMP (Windows, Icons, Mice, Pointer), while Advanced styles involve techniques such as Virtual Reality (VR), Augmented Reality (AR) and Natural Language. These categories will help researchers to explore the value that a visualisation-oriented chatbot can add to these interaction styles. In the literature, the use of different interaction styles varies. Most V-NLIs use both Basic (WIMP) and, naturally, Advanced (NL) interactions 
, while some of them use only Advanced (NL) interactions 
Figure 4. Interaction Space affects all the steps of the visualisation pipeline.
Regarding the bibliography, the most used interaction techniques are select and filter. V-NLIs such as  use NL to interact with visualisations selecting (marking a data point) and filtering (showing something conditionally) data according to user queries. Similarly, Refs.  use NL to update visualisations using filtering at the data transformation stage, while Chat2Vis  does this to generate visualisations. Others, such as , use both direct manipulation and NL to filter visualisations at the visual mapping stage. Refs.  use both basic and advanced interaction techniques at the visual mapping stage to filter visualisation and  uses advanced interaction while using the select method. Orko  and Databreeze  use both NL and direct manipulation to filter and select data on visualisations. The next most used method is Encode . For example, a lot of works  allow users to colour and size data points and add/remove attributes by using Basic (WIMP) and Advanced (NL) interactions at the visual mapping stage. The reconfigure method is supported by four V-NLIs , which are used to change the visual perspective of the data in the visual mapping. For instance, Valetto  uses gestures (a basic interaction) to flip the axis in the visualisation mapping stage.
Furthermore, the explore method, which is considered to be zooming and panning in the View Transformation stage, is used in four V-NLIs , all with basic interactions. The abstract/elaborate method is used in four V-NLIs  to drill down to show more details. For example, Ref.  transforms data to show average hours of sleep over various months, and users can choose the visual mapping to see each month separately in more detail using NL. Similarly, Ref.  uses NL to do drill downs, while, on the other hand, TransVis  uses direct manipulation. InChorus  uses both modalities. Finally, the connect method is only used by two V-NLIs . Both of these V-NLIs have network visualisation and use the connect method to highlight the relationships between links using Advanced interactions (i.e., using Focus+Context visualisations). While performs this at the data transformation stage, Ref.  does this at the visual mapping stage.
Chatbots are software systems able to engage in conversations with users 
, thereby representing a natural interface for them. In Figure 5
propose a general characterisation of chatbots using four dimensions, named AINT, depending on how researchers
view them. First, chatbots may have Anthropomorphic (A) properties such as appearance 
and gender and also may be endowed with personality and emotions 
. Second, as an Intelligent system (I), task-based chatbots can proactively make data-driven decisions to give support to users’ activities, and social chatbots maintain meaningful and engaging conversations with their users. In any case, chatbots can also be enhanced through a variety of AI methods and techniques, for example predicting users’ necessities and behaviours and thereby personalising the UX (User eXperience) 
. Third, as a Natural language processing system (N), chatbots usually consist of an NLU (Natural Language Understanding) part 
, which understands the intentions (goals) of the users (i.e., the inputs), maintaining the visual context of the conversation, but they must also provide a textual, visual, auditive answer to them, based on that context. Those answer types (i.e., the outputs) can be either predefined or automatically generated. In the specific case of text, they are usually created by an NLG (Natural Language Generation) system 
. Finally, as an interactive system (T), chatbots can be integrated with different interaction styles (WIMP, VR, XR) and be equipped with a multimodal interface through voice, text and gestures.
Figure 5. AINT—General characterisation of a Chatbot based on four dimensions: A—Anthropomorphic, I—Intelligence, N—Natural Language Processing, and T—inTeractivity.
Visualisation-oriented Natural Language Interfaces (V-NLIs) are interactive systems (AINT) designed to facilitate the users’ visual analytic tasks. They can be designed using two different user interfaces (UI): a form-based interface and a chatbot-based interface. On one hand, a form-based V-NLI  is usually composed of a text box that allows the users to introduce the visualisation query using natural language, though it also has other widgets, for example, to refine (filter) the resultant visualisation. The most know approaches are Form-based V-NLIs . On the other hand, a chatbot-based interface [63 ] is distinguished by a named entity (also known as an agent), with gender and appearance, as well as with the ability to recognise and express emotions, while having personality traits (i.e., empathetic, fun, neutral). Among the existing works, some integrated Chatbot-based V-NLIs . These V-NLIs have a chat window in which users can engage in conversations with a bot to analyse data visualisations. In some tools, the chat window is separated from the main visualisation dashboard  , and in others, the visualisations are displayed in the chat windows . For instance, both Iris  and Ava  were developed to help users perform complex data science tasks such as statistical analysis. While  displays visualisations in a single chat window, Ref.  has two windows, one containing the chatbot and the other showing the actions the chatbot performs, such as displaying visualisations.
The types of inputs (analytical questions) that a V-NLI system deals with are low- and high-level queries. In Low-level queries, the users explicitly describe their intent, for example, “Show me action films that won an award in the past 10 years”. Therefore, these queries can be interpreted easily. When researchers explored different Query Types, researchers found that most of the previous research presented V-NLIs that support only low-level queries . For instance, in works such as , users can ask direct queries and receive answers such as filtered or highlighted data points on visualisations or new visualisations. Moreover, there are V-NLIs that have more specific datasets and the chatbot is designed to ask users questions or give prompts to perform the analysis . In contrast, High-level open-ended queries are naturally broader and their interpretation can be more complex . There are two V-NLIs support both low and high level queries, Talk2Data, which is form-based  and Chat2Vis, which is chatbot-based .
Moreover, queries can be One-turn or Follow-up. In One-turn
queries, the users ask the system in a single shot. On the other hand, the users usually perform Follow-up queries, which are a series of interconnected questions . There are only four V-NLIs that support follow-up queries  and all of them support only low-level queries. After each query, Ref.  recommends follow-up queries on a list. On , users can refer to entities using determiners and pronouns.
The challenges of both understandability and discoverability require an interactive conversational system to guide the users on how to effectively communicate their goals (also referred to as intentions). Well-known Conversational Guidance
strategies are based on help
—the chatbot gives the users hints on what to ask; intent auto-complete
functions—the system makes suggestions of possible intents while the users are writing the intent 
; and intent recommendations 
—after giving a response, the system suggests, based on data or on the previous turns of the analytical conversation, possible next intents to the users. Additionally, the understandability problem of NLIs is mainly derived from the biggest challenge that NL poses, which is ambiguity. One solution is to ask the users what they meant or to use disambiguity widgets 
. Some existing tools do not provide  the user with any conversational guidance, while the rest of the tools recommend tasks or queries , help users , or auto-complete queries  designed to increase the discoverability of the NLI, helping users to understand what the NLI is capable of doing. Moreover, an interesting case of co-reference arises when natural language interfaces coexist with other interaction styles (Multimodality) such as menu selection (WIMP—Window Icon Mouse Pointer) and direct manipulation (XR—Virtual or Augmented Reality) . Some reviewed V-NLIs have additional Multimodality to Natural Language(NL). For example, Refs.  have ambiguity widgets with which users can interact. Moreover, with V-NLIs , users can interact with the user interface using touch.
Users can also select filters and interact with data without using NL.
In addition to the requested visualisation, a V-NLI can consider Complementary Output such as Feedback, either text or visual: (i) to inform about the query’s success or failure, (ii) to justify relevant decisions taken by the system, (iii) to provide the users with additional explanations to better interpret the resulting visualisation (textual, oral, graphs, or statistics) and annotations, and (iv) to display changes in the User Interface (highlighting menus, buttons).
All of the works explored give the users textual feedback and some of them give visual feedback as well. The only exception is Chat2Vis , which, probably due to its recentness, is not yet integrated into a visualisation platform. Basically, textual feedback is used to inform or justify chatbot decisions to the users. Works such as  inform users about the success or failure of their queries. Moreover, Refs.  provide the users with informative feedback, additional explanations and follow-up questions to users to carry on the analysis. Furthermore, researchers explored related work that provided users with additional visual feedback, such as supplementary graphs with main visualisation or changes on filters on the UI that have been applied by the chatbot. V-NLIs such as  have visual feedback on the UI.
4. Contrasting V-NLI: Queries, Transformations, Multimodality.
Figure 6 shows the contrasted the results of the input characteristics of V-NLIs with how these systems deal with the data space stage in the visualisation pipeline. Researchers found that most of the works allow the users to express only low-level queries, and those that consider high-level queries do so with simple data types (see Figure 6, signal a and b) and attributes, i.e., tabular data with numerical and nominal attributes. Moreover, independently of the user’s intents (low or high queries), all the examined V-NLIs contemplate simple data transformations (i.e., simple aggregations and statistical analysis such as correlations and logistic regressions). Note also that those simple data transformations have normally been incorporated into V-NLI systems that consider followup queries . Few works provide users with help or recommendations based on the data type, which is currently mainly tabular data . Regarding multimodality, most systems allow user–chatbot interaction combined with WIMP, but few of them allow touching  and only one work uses gestures .
Figure 6. Spider chart displaying the relationship between data types and input V-NLI characteristics.
Figure 7 shows the scope of advanced and basic visualisations in both V-NLI and visualisation dimensions; see the borders in purple and green colour, respectively. As researchers can appreciate in the magenta- and blue-coloured polygons, V-NLIs that consider basic layouts embrace these dimensions in greater measure than those considering advanced layouts. Furthermore, the empty space of the spider reveals that there is a lot of room for research on different aspects of both basic and advanced visualisations in V-NLIs.
Figure 7. Spider chart displaying the relationship between Visual Space and V-NLI characteristics of analysed works.
Figure 8, both chatbot-based and form-based approaches cover a similar, short range of interactive methods—Filtering and Selecting being the most covered—including some values near zero, especially with chatbot-based approaches (see the complex interactions Abstract/Elaborate , Connect , Reconfigure , Explore  in yellow dots).
Figure 8. Spider chart displaying the relationship between the type of V-NLIs and interaction methods.