THINK-AI DOCUMENTATION
Ruth
2024-09-10
A. Introduction
1.THINK-AI Overview
The platform also enables the creation of survey questionnaires and polls for collecting data from a variety of stakeholders, such as respondents, prospects and customers. We designed THINK-AI with the aim of making data exploration and management more intuitive and accessible to all those seeking to fully exploit the value of their data. Each user with an account can customize a dashboard, where generated graphs and reports can be saved and managed.
This guide gives a detailed overview, with illustrations, of the platform’s various functionalities and how to make the most of them.
2. Target audience
3. Key features
THINK-AI’s functionalities fall into three main categories:
■ Mathematical model generation: The platform integrates artificial intelligence algorithms for the creation and analysis of predictive and explanatory models, optimizing data-driven decision-making,
■ Surveys and surveys: THINK-AI offers tools for the creation and management of questionnaires and surveys, enabling the collection, analysis and interpretation of qualitative and quantitative data from various target populations.
4. Official website
B.Connecting to THINK-AI
To connect to THINK-AI for the first time, you need to create a user
account by going to the Create an account menu on the
home page. After completing the registration form, validate your
registration by clicking on Register. Once registration
is complete, you will be able to log in using the identifiers created at
this stage.
We recommend that you keep your login details in a safe
place, as they will be required for all future connections to the
platform.
The images below illustrate the process of creating an
account on THINK-AI:
■ Go to the Create an account menu on the home
page,
C. User interface
THINK-AI’s user interface is structured around two main
menus:
■ The Summary menu, which lets you manage and
manipulate datasets. This menu is subdivided into several key
submenus:
★★ Dashboard : provides an overview of generated
data and graphs, making it easy to navigate and quickly analyze
important information,
★★ Data : enables management
of datasets used for visualization and modeling,
★★ Contact
: provides the information needed to contact the platform’s
technical or sales support,
★★ Documentation :
contains detailed guides for using the platform and its features,
★★
Pricing : details the platform’s various pricing
offers, according to users’ needs.
■ The Poll and Survey menu is dedicated to the
creation of questionnaires for surveys and polls, enabling responses to
be collected from participants. This menu offers an intuitive interface
for designing customized questions and distributing surveys to
respondents.
D.Summary tab: Its menus and functions
1. Data menu
The Data menu provides functions for the efficient manipulation of data sets. The various options available in this menu are described in the following paragraphs.
1.1 Import data sets
This feature allows you to import datasets from various sources into
the THINK-AI platform. Supported dataset formats include:
■ Excel (.xls, .xlsx),
■ Text files (.csv, .txt, .json),
■
Relational Database Management Systems (RDBMS) (.sql).
a. Importing a .csv data set
The steps for importing a .csv dataset into THINK-AI are as
follows:
■ Navigate to Summary > Data > Data
connection,
■ Select as Data source
csv,
■ Click on Browse to choose the file to import
(in the example below, we select the nutrition2.csv data set),
■
Choose the appropriate Column separator (in this
example, we use the comma, as our data set is comma-delimited),
■
Check Header if you want the first row of the data set
to be considered the column header (this option is enabled in the
example below),
■ Check or uncheck Delete spaces as
required (this option is enabled in the example below),
■ Check or
uncheck Omit ’' as required (this option is unchecked
in the example below),
■ Check or uncheck Ignore quote
“ as required (this option is disabled in the example
below),
■ Under Missing value, select the treatment
to be applied to missing data,
■ Under Comment,
select the treatment to be applied to comments,
■ Finally, click on
Import/Update to validate the import of the .csv data
set.
b. Import an .xls data set
The steps for importing an .xls data set into THINK-AI are as
follows:
■ Navigate to Summary > Data > Data
connection,
■ Select Data source as
xls,
■ Click on Browse to choose the data set to
import (in the example below, we select the data set
Flowers-Iris.xls),
■ Check Headers if the data set
contains headers,
■ In Sheets, choose the sheet to
export if the data set contains several sheets (in this example, our
data set contains a single sheet named data),
■ In Data
name, enter the desired name for the sheet to be loaded,
■
In Cell range, specify the range of cells to be
imported,
■ In Ignore rows, specify the row number
to be ignored if necessary,
■ In Missing value,
specify the values considered to be missing,
■ Check First
row as column name if necessary,
■ Finally, click on
Import/Update to validate the import of the .xls data
set.
c. Import an .xlsx data set
d. Import a .txt data set
e. Import a .json data set
The steps for importing a .json dataset into THINK-AI are as follows
:
■ Navigate to Summary > Data > Data
Connection,
■ Select as Data source
json,
■ Click on Browse to select the dataset to
import (in the example below, we select the sample_data.json file),
■ Check Headers if the dataset contains headers,
■
Finally, click on Import/Update to validate the
import.
f. Import a .sql dataset
The steps for importing a .sql dataset into THINK-AI are as follows
:
■ Navigate to Summary > Data > Data
connection,
■ Select sql as Data
source,
■ In Drive, choose the
corresponding drive from those available in the drop-down list,
■ In
Database name, specify the name of the source database
(containing the data to be read),
■ In Database
server, specify the name of the database server,
■ In
Connection ID, enter the user name for accessing the
database,
■ In Password, enter the database
connection password,
■ Under Data name, specify the
name to be given to the data to be read,
■ List of old
data displays any old data read,
■ Under Data
description, specify a description of the data,
■ Under
SQL code, write the SQL code to be executed to extract
the data from the source database,
■ Finally, click on the
Execute/Refresh button to execute the SQL code in the
source database.
1.2 Transforming a data set
Dataset transformation refers to the processes by which the dataset
is modified, enriched or converted into a format more suitable for
analysis or visualization. To transform a dataset in THINK-AI, it must
first be imported (see section Import data) and the
following steps followed:
■ Click on the Data transformation menu under
Data,
■ Under Column names, the
names of the various columns in the imported dataset appear,
■ In
Data type, the type of each column is displayed and can
be modified as required,
■ Check Delete missing
values if the column concerned contains missing values,
■
In Replace missing values, indicate the value to be
used to replace missing values,
■ Under Rename
column, enter the desired new name for the column,
■ Click
on the eye icon under Summary to view a preview of a
specific column,
■ Finally, click on the Update
button to validate the transformations performed.
N.B.: The Search box at top right allows you to quickly find a column by entering its name.
1.3 Summarizing a data set
After importing and/or transforming a dataset, click on the
Data summary menu to obtain descriptive statistics for
each column, according to their respective type. By selecting a variable
under Variable in row and another under
Variable in column, then specifying the Graph
type, it is possible to visualize the relationships between the
two variables chosen.
The figure below shows a summary of the Flowers-Iris dataset.
THINK-AI also allows you to download this summary in several formats, as
shown in the figure.
1.4 Running an R script on a dataset
THINK-AI allows you to write and run R scripts on imported and/or
transformed data. To do this, simply go to the R menu
and follow the steps below:
■ Write the R script in the dedicated console (left),
■ Click on
the Run button to execute the R script (execution
results appear on the right),
■ Click on the Save
code button if you wish to keep the script you have
written,
■ Finally, if necessary, click on Clear
console to delete the contents of the console.
1.5 Displaying a data set
a. Hide columns of a data set
To hide certain columns in a dataset, proceed as follows :
■ Access the Column visibility submenu, then tick
the columns you wish to hide,
■ Uncheck the columns
you no longer wish to display.
To redisplay hidden columns, simply
tick them again.
In the following example, we choose to hide the
sepal_width and petal_width columns of
the Flowers-Iris dataset.
b. Transforming columns in a data set
The Transform submenu lets you perform the following
transformations on the columns of a dataset:
■ Rename column,
■ Descriptive statistics for the column,
■
Change column type,
■ Format column,
■ Create a new column,
■ Replace values,
■ Delete column.
Let’s examine these transformations one by one.
b.1. Rename column
This function allows you to change the name of a column within a
dataset. To do so, simply enter the new name in the appropriate field,
then validate the operation.
b.2. Univariate and multivariate column analysis
This function allows you to generate statistical analyses for a
column or set of columns in a dataset. It is divided into two parts:
univariate and multivariate
analysis.
■ Univariate analysis
Univariate analysis
provides a comprehensive statistical summary of the values in a single
column. This includes descriptive measures such as :
★★ Central trend: Mean, median, mode,
★★
Dispersion: Standard deviation, variance, amplitude,
quartiles,
★★ Distribution: Histograms or density
diagrams (depending on data type).
These indicators provide an
overview of the main characteristics of the column under study.
For example, in the image below, the univariate analysis of the
OBS column of Flowers-Iris shows a summary of values
with statistics calculated for each element.
■ Bivariate analysis
Bivariate analysis enables
the study of relationships between two variables through pivot tables
and corresponding visualizations. This enables us to understand the
correlation or interaction between two columns in a data set.
★★ Creating a pivot table
To generate a pivot
table, follow these steps:
★★★ select variable for columns under Choosing variables in
columns,
★★★ select variable for rows under
Choosing variables in rows,
★★★ Define variable to
aggregate under Value to aggregate,
★★★ Choose
Aggregation function (sum, average, count, etc.),
★★★ Use Filter field to add filter conditions or
criteria,
★★★ Click Generate dynamic table to
produce results.
The illustration below shows the process of creating a pivot table on
the Flowers-Iris data set.
★★ Application of filters on pivot tables
To
refine your analysis, you can add conditional filters to pivot tables.
The steps involved in creating a filter are :
★★★ Select the column to be filtered under Filter
column,
★★★ Choose the appropriate Filter
function (e.g. equal to, greater than),
★★★ Enter formulas
or expressions in the editing area,
★★★ Validate by clicking on
Validate or cancel with Cancel.
The image below shows the process of creating a filter on a pivot
table.
b.3.Changing a column’s data type
To change the data type of a column, select the desired new type from
the drop-down list below the Change type option. This
feature allows you to adapt columns to specific analysis or
visualization requirements.
An illustration of this process is shown below.
b.4.Modifying column format
To change the format of a column, access the Column
formatting option and select the desired format type from the
available choices. Next, enter any additional information required in
the appropriate fields. Finally, click on the button at the bottom of
the window to Validate column formatting.
b.5.Substituting values in a column
To substitute values in a column, select the Replace
values option and follow the steps below:
■ In the Values to replace field, specify the values
targeted for replacement,
■ Under Replacement
values, specify the new values to be entered,
■ Finally,
click on the Validate value replacement button at the
bottom of the window to confirm your changes.
The above steps are illustrated below.
b.6.Creating a new column
This feature lets you create a new column from a
function applied to a source column, or recode an existing
column.
■ To Create a new column, follow the steps below:
★★ In Choice functions to apply to source column,
select the desired function,
★★ In Source column
name, specify the name of the original column,
★★ In
New column name, specify the name of the new
column,
★★ If a specific condition must be met before the new column
is created, activate the If condition option and fill
in the necessary information (optional),
★★ Finally, click on the
Validate new column creation button to confirm your
changes.
■ To Re-code an existing column, proceed as
follows:
★★ In Source column name, specify the name of the
column to be modified,
★★ In New column name,
specify the name of the new column,
★★ Click on Category
coding, fill in the required fields, then click on the
Validate button to confirm the conditions for re-coding
the old column,
★★ Finally, click on the Validate
button to finalize the creation of the new column..
b.7.Deleting columns
To delete a column, select Delete Column. A
confirmation message appears on the screen; click OK to
validate the deletion, or Cancel to refuse the
operation.
Here’s an illustration of this feature.
c. Save the state of a data set
The Status Backup feature allows you to capture and
store different versions or configurations of a dataset. This makes it
possible to restore a previous state at any time, for rigorous tracking
of changes.
To do this, access the Save Status menu
and follow the steps below:
■ Select Create state,
■ Assign a name to the
report using the Rename field,
■ Confirm (or
cancel) the creation of the state using the Rename (or
Cancel) button.
Illustration below.
Once created, the data set’s state can be edited
(via the edit button, framed in red in the figure below),
updated (button framed in green), or
deleted (button framed in blue). These actions enable
flexible and controlled management of saved states.
d. Transpose a data set
By clicking on the Transpose option, the rows and
columns of the dataset are inverted: rows become columns, and columns
become rows. This operation restructures the way data is organized and
visualized, facilitating analysis from a different angle.
Below is an example of the transposition of the Flowers-Iris dataset.
e.Empty dataset field
The Erase option temporarily deletes the contents of
the active dataset. To restore the deleted data, it is necessary to
reload the dataset from its source.
This feature is illustrated below.
f.Save changes to a dataset
The Save option saves all changes made to the
current dataset. This action ensures that any adjustments or updates are
taken into account for future use.
Below is an illustration of this feature.
g. Adding new rows to a data set
To add a new line to a dataset, proceed as follows:
■ Click on the Create menu,
■ Fill in the
various fields with the required information,
■ Confirm (or cancel)
the addition of the new line by clicking on the Create (or
Close) button.
Below, an illustration with the Flowers-Iris dataset.
h. Edit rows in a dataset
To edit the information on an existing line in a dataset, follow
these steps:
■ Select the line to be edited,
■ Click on the
Edit menu,
■ Make the necessary changes to the
fields in the selected line,
■ Validate (or cancel) the changes by
clicking on the Edit (or Close) button.
i. Deleting rows in a data set
To delete rows in a dataset, follow these steps:
■ Select the line to be deleted,
■ Click on the
Delete menu,
■ A confirmation window opens to
confirm that the line has been deleted,
■ Click on the
Delete (or Close) button to confirm (or cancel) the
operation.
j. Download data table
To export a data table, select the desired file format from the
available options:
■ Excel (red box in the figure below),
■
CSV (green box in figure below).
These options save the data table in the appropriate format for later
use.
k. Copy data table
To copy the data table, click on the Copy menu in the top left-hand toolbar, to duplicate the table contents for use in other applications.
l. Filter a column in a dataset
Filtering can be applied to a column by entering the desired filter
value in the input field below the column name.
In the example
below, we have chosen to filter only Setosa species in
the SPECIES column of the Flowers-Iris dataset.
m. Undo/redo a dataset modification
To undo or redo a modification on a dataset, click on the backspace (green box in the figure below) or restore (red box in the figure below) buttons respectively.
n. Rename a dataset
To rename a dataset, simply double-click on its
current name.
In the example below, the Flowers-Iris dataset is renamed to Iris.
o. Sort values in a dataset
Column values can be sorted in ascending or descending order. To do
this, use the directional buttons next to each column header.
In the example below, we sort the values in the
PETAL_WIDTH column in descending order.
p. Search for values in a dataset
The Search menu lets you quickly find specific
values in the data table.
In the example below, we’re looking for the string
versi in the Flowers-Iris dataset, which contains the
full value versicolor.
2. Dashboard menu
The Dashboard menu lets you create dashboards from imported data. A
dashboard is a visual management tool that summarizes key information,
making it easier to track data trends and take informed decisions thanks
to an overview.
This menu also offers a chat interface with the AI to assist the user
in performing complex tasks.
The Dashboard menu is accessible under the Summary
tab. Below is an illustration of this menu.
Let’s take a look at the different functions offered by THINK-AI via
the Dashboard menu.
2.1 Creating a dashboard
To create a dashboard, simply click on the + icon
under the Dashboard menu.
Une illustration de ce
processus est présentée ci-dessus.
2.2 Naming the dashboard
To rename a dashboard, proceed as follows:
■ Click on the meatballs (the three dots at the top
left of the dashboard name),
■ In the Rename
window, enter the desired new name for the dashboard.
2.3 Modify a dashboard title
You can also change the font, size,
font color and background color of a
dashboard title. To do this, proceed as follows:
■ Click on the meatballs,
■ Access the
Window title fonts submenu,
■ Select the desired
font, size, background
color and font color.
2.4 Adding a description to a dashboard
To add a description to a dashboard, follow the steps below:
■ Click on the meatballs,
■ Access the
Add title/description submenu,
■ An edit box will
appear, where you can enter the descriptive text corresponding to the
dashboard.
2.5 Changing the color of a dashboard
To change the color of a dashboard, follow these steps:
■ Click on the meatballs,
■ Access the
Table color submenu to select the desired color to
apply to the dashboard.
2.6 Use artificial intelligence to interpret tables and graphs
To overcome human limitations in the analysis and interpretation of
complex data, THINK-AI offers users artificial intelligence support.
This advanced solution not only interprets Data, but
also processes Figures and answers General
Questions.
In THINK-AI, to access the exchange interface with artificial
intelligence you need:
■ Click on the meatballs (the three dots at the top
left of the dashboard name),
■ Select Add a
title/description,
■ At the top right of the editing area,
click on the search icon,
■ In the Target
object section, specify the framework for interaction with AI
(choose from Data, Figures, or General question),
■ Choice
of object to interpret: please specify inputs,
■
Choose variables: identify the variables in the dataset
on which the AI will focus. This column will only appear when
Data is selected as object to be
interpreted,
■ In Post a question, write
the prompt,
■ Finally, click on Generate answer to
submit the query to the AI.
2.7 Saving a dashboard
To save a dashboard, click on the save button in the
horizontal bar.
The following figure illustrates this option.
2.8 Update a dashboard
To update a dashboard after modifications, click on the
Update button located in the horizontal bar.
2.9 Download a dashboard
THINK-AI offers users the option of downloading their dashboard in
HTML format simply by clicking on the HTML Document
button.
A presentation of this option is provided below.
2.10 THINK-AI visualizations
a. Overview of THINK-AI’s visualization options
a.1 Interactive figures (🖱️📊)
■ Description : This type of interactive
visualization allows users to interact directly with chart elements, for
example, hovering over data points to display additional information, or
selecting specific segments for further analysis. These charts are
particularly effective in dynamic dashboards, facilitating visual
exploration of data in real time..
■ Categorization: This visualization is applicable
to all types of data, whether quantitative or qualitative, but is
particularly suitable when the analysis involves complex or voluminous
datasets. It is ideal for scenarios where interactive visual exploration
is required to identify specific trends or insights.
a.2 Histograms(📉)
■ Description : A histogram is a graphical
representation that groups continuous quantitative data into classes (or
bins).Each bar in the histogram represents the frequency or density of
observations in a given class. The height of each bar indicates the
concentration of data in that specific range, facilitating analysis of
the distribution of values.
■ Categorization: Recommended for visualizing
continuous data, the histogram is particularly effective for examining
the distribution of a set of numerical values. It is often used to
illustrate the distribution of attributes such as age, income or
scores.
a.3 Bar (Bar graph) (📊)
■ Description: The bar chart visualizes data as
vertical or horizontal bars, where the length of each bar is
proportional to the value of the data represented. This type of chart
makes it easy to compare values between different categories or
groups..
■ Categorization: This chart is mainly used for
categorical data. It is particularly suitable for comparing distinct
groups, such as product categories, countries, or market segments, by
directly displaying the differences between each category..
■ Data types: The bar chart is ideally suited to
qualitative data, where each category can be associated with a numerical
value. It is often used for comparisons between groups, for example,
sales distribution by region, the performance of several products, or
event attendance by day..
a.4 Scatter plots (🔵🔴)
■ Description : The scatter plot is a graph in which
each point represents an observation in two-dimensional space, with one
variable plotted on the X-axis and another on the Y-axis.It helps to
visually identify relationships and correlations between two continuous
variables.
■ Categorization: This graph is ideal for examining
relationships between two quantitative variables.It is often used in
regression analyses or to observe trends and patterns between correlated
variables.
a.5 Line (Line graph) (📈)
■ Description : The line graph connects data points
by lines to show the evolution of a continuous variable over time or
over a given interval.It is particularly effective for visualizing
trends or continuous changes.
■ Categorization: Suitable for temporal data or
continuous series, where it is necessary to track variations or trends
over time.It is often used in time series analysis.
a.6 Mustache box (Boxplot)(📦📉)
■ Description: The boxplot (whiskers box) is a graph
that visualizes the dispersion of a set of quantitative data by showing
quartiles and identifying outliers.It gives a quick overview of the
distribution, median, and variation of the data.
■ Categorization: Used to summarize and compare
multiple sets of quantitative data. It is particularly effective for
analyzing data dispersion and identifying extreme values.
■ Data types: This chart is recommended for
continuous numerical data, such as measurement series (e.g. salaries,
test scores, etc.). It is often used to compare the distribution of data
between different groups or categories.
a.7 Area (Area graphics) (🌐📈)
■ Description : The area chart is a variant of the
line chart, with the area below the line filled with color.This
visualization helps to highlight the cumulative proportion or quantity
of a value over a given period.It is used to visually show data
accumulations while making it easier to perceive trends.
■ Categorization: This type of graph is ideal for
continuous or temporal data, and is often used to visualize
accumulations or proportions, such as the progressive contribution of a
variable to the total.
a.8 Table (📋)
■ Description: The data table displays information
in structured table form, allowing users to read the exact values of
observations directly.This is a very simple and effective method of
presenting detailed data, offering a complete and accurate view of
values in their raw form.
■ Categorization: The table is suitable for all
types of data, whether quantitative or qualitative, and is particularly
useful when it is essential to show exact values to enable detailed
comparisons or analysis.
■ Data types: This format is ideal when data needs
to be compared precisely, or when a complete overview of the raw data is
required.It is frequently used for detailed reports, inventories or
presentations where every data point counts, as in financial studies or
production databases.
Now that we’ve explored in detail the different visualization options
available in THINK-AI, it’s essential to understand how to choose the
right type of graph depending on the nature of the data to be analyzed.
This will maximize the clarity of insights and make the interpretation
of results more efficient.
Here is a summary of figure types and tips for their
optimal use depending on data categorization.
★★ Categorical (qualitative) data
★★★
Bar chart : To compare separate categories.
★★ Continuous (quantitative) data
★★★
Histogram : To visualize distribution,
★★★
Nuage of points : To observe relationships between two
variables,
★★★ Line graph : To show trends over
time,
★★★ Boxplot : To summarize and compare
distributions.
★★ Time (chronological) data
★★★ Line
graph : For time series,
★★★ Area chart :
To show cumulative trends.
★★ View interactive data
★★★
Interactive figure : To explore complex datasets
dynamically.
★★ Data table ★★★ Table: For a
tabular view of raw data, or precise comparisons.
b.Clustering factors
Grouping** is a concept in mathematics and statistics that involves
organizing elements according to certain characteristics or criteria.
This makes it possible to analyze, summarize or simplify data
sets.
Grouping factors must be carefully selected according to the nature
of the data and the objectives of the analysis. Here are some
suggestions for choosing a relevant Grouping Factor
among the columns of a data item:
■ Defining the objective of the analysis
Before
choosing a grouping factor, it’s essential to clarify the objective of
your analysis.
Example: If you want to analyze customer
satisfaction, you might be interested in grouping factors such as
“product category” or “geographic area”.
■ Check available columns
★★ Analyze
columns : Review the columns in your dataset and identify those
that might be relevant to your analysis objective. Consider:
★★
Data types : Are they numerical (such as income, age)
or categorical (such as gender, product type)?
★★ Importance
of columns : Some columns may be more important than others,
depending on your search question.
■ Considering data variability
For example, a
factor with many categories (such as “product type”) is often more
informative than a factor with few categories (such as “status” if it
has only two values).
Example: If you have an “age” column with a
wide range of ages, consider grouping it into age brackets (0-18, 19-35,
etc.) to better visualize trends.
■ Evaluating relationships between columns
Think
about how the columns might interact with each other.A good grouping
factor might reveal interesting relationships between different
variables.
Example: If you have a “region” column and a “sales
amount” column, grouping by region can highlight differences in
performance between regions.
Choose grouping factors that maintain the clarity of your visualization. Too many categories can make interpretation difficult.
Example: In a bar chart, too many categories can make the chart overloaded. Make sure that each category is significant enough.
C.Concept of dividing factors
In the context of data visualization, dividing factors play a crucial
role in segmenting data according to relevant criteria to better
understand trends and relationships. The selection of these factors must
be methodical and aligned with analytical objectives. Here are some
recommendations for choosing effective splitting factors:
■ Alignment with analysis objectives
Before
choosing a division factor, clarify the objectives of your analysis. A
relevant division factor should help answer the specific questions you
wish to explore. For example, if the objective is to evaluate sales
performance, factors such as “product category” or “market segment” may
be useful..
■ Explore available columns
Take a close look at
the columns in your dataset. Identify those that might be relevant to
your analysis. Consider:
★★ Data Types:
Differentiate between numerical data (such as income) and categorical
data (such as regions).
★★ Relevance : Some columns
may offer more significant insights than others, depending on the
context of your analysis.
★★ Data variability: Opt
for division factors with sufficient variability. A factor with many
categories (such as “product type”) is often more informative than one
with few distinct values. For example, grouping ages into brackets
(0-18, 19-35, etc.) can help identify consumption trends specific to
each bracket.
■ Interrelation between columns
Evaluate
potential relationships between columns. An effective division factor
can reveal significant interactions. For example, analyzing sales by
region and product type may reveal distinct buying behaviors in
different geographic areas.
Choose dividing factors that promote clarity in your visualization. Too many categories can make interpretation difficult. Make sure that each category is significant enough to provide insights without overloading the chart. For example, in a bar chart visualization, too many categories can impair legibility.
d.Visuals generation
To generate a custom visual in THINK-AI and integrate it into an
existing dashboard, please follow these steps:
■ Access the Figure creation form available under
the Dashboard menu,
■ In the List of
uploaded data section, choose the dataset containing the
information you wish to view,
■ Type de figures:
spécifiez le type de visuel le plus adapté à vos données (voir section
a. Overview of the different visualization types in
THINK-AI),
■ Select X axis: specify the
numerical or categorical variable to be represented on the x-axis,
■
Select Y axis: select the variable to be displayed on
the ordinate,
■ Z grouping factor: if necessary,
specify a variable for grouping the data (see section b.
Grouping factors),
■ Figure division
factor: define the criteria for subdividing the visual (see
section c. Division factors),
■ Filter
factor: apply filters to refine the selection of data to be
visualized,
■ Click on the Generate figure button.
The system will proceed to create the visual according to the parameters
defined,
■ In the Assign figure to dashboard
section, choose the target dashboard where you wish to add the
visual,
■ Click on the Send figure button to
finalize the operation.
Note: The newly created visual will automatically be added to the
list of generated figures, enabling you to find and
modify it later if required.
Below is an illustration of the steps outlined above.
2.11 Customized, interactive visuals
2.11.1 Horizontal toolbox
a. Zoom icons
■ Zoom in(+): enlarges the view of content, allowing
the user to see more detail;useful for examining specific elements of a
visual,
■ Zoom out(-): reduces the size of the
view, allowing the user to see the bigger picture or access a wider
range of content.
b. Reset icon(🏠)
c. Add notes icon(📝)
■ Add text: allows you to add annotations or
comments directly on the visual to highlight particular points or
provide additional explanations.
d. Backup icon (📷)
■ Download graphic: saves the visual in image format
(PNG).
e. Deletion icon(🗑️)
■ Delete selected graphic: deletes the visual from
the dashboard.
f. Rectangular selection icon
■ Rectangular selection: allows the user to select
and isolate a specific region within a graph or visual. Rectangular
selection can be used to zoom in on a particular section of the graph to
see more detail on that specific region of data.
Data values within
the selected area can be automatically highlighted or displayed for easy
reading.
This feature is particularly useful in large data
visualizations, where the user needs to focus on a specific area without
losing sight of the rest of the graph.
g. Lasso selection icon
■ Lasso selection: allows the user to draw a free
shape around a region of a graph to select specific data points. Unlike
rectangular selection, which is linear, lasso offers greater flexibility
in selecting elements that do not follow a rectangular pattern.
The user can manually draw a curve around the elements of interest,
which is particularly useful for isolating points in a dense or
irregular graph. This is particularly useful for isolating points in
dense or irregular graphs.
Below is an illustration.
h. Translation icon
■ Translation: allows the user to move the display
of a graph or visual without altering the zoom or proportions of the
current view. This feature is useful when the user wishes to examine
another part of the graph without losing detail, while retaining the
scale and zoom level already set.
By holding down the translation icon, the user can click and drag on
the graph area to move the display horizontally or vertically. This
function is often used in conjunction with the zoom options to enable
precise, controlled exploration of complex graphics.
i. Auto-scale icon
■ Auto scale: automatically resets and adjusts the
scale of a chart or visual to suit the current size and dimensions of
the window or display area. This ensures that all represented data are
visible in a complete, optimized view.
Scale reset : When actions such as zooming in,
zooming out or translating have been performed, this icon allows you to
return to a default view where all data is visible, without the need to
manually adjust axes.
Dynamic scaling : The
auto-scale icon automatically resizes axes and displayed values, which
is particularly useful after exploring certain parts of a graph with
other tools such as the lasso or translation.
Optimized
visibility: By activating this icon, the user ensures that the
extremities of data and all important points are included in the view,
thus avoiding leaving information outside the chart’s
boundaries.
Auto-scale** is therefore essential for complex graphics or when
frequent manipulations are performed, as it offers a quick method of
refocusing and displaying all data at once.
j. Icons for undoing/redoing an action
In THINK-AI, the Cancel and Redo
functions play a key role in enabling users to easily correct errors or
reverse decisions.
■ Cancel change(green box in figure below): when the
user clicks on the Cancel icon, it returns him/her to
the state prior to the last change made. This feature is particularly
useful for reversing an accidental action or unwanted change.
■ Do over (red box in figure below): conversely, the
Do over icon allows you to re-apply a cancelled action.
By selecting this option, the user restores the state following the
modification, giving him the flexibility to test different
configurations without fear of losing his work.
These two icons ensure that actions run more smoothly, and enhance
the user experience by making errors easily reversible.
k. Display refresh icon
This feature is particularly useful in contexts where the data
displayed is dynamic or likely to change frequently, for example when
viewing dashboards, graphs or real-time data streams.
By clicking on the refresh figure icon, the user
triggers an immediate update of the data or visual displayed, ensuring
that the information presented is up to date.This is a quick and easy
solution for retrieving the latest data without having to reload the
whole interface, improving efficiency and reducing waiting time for the
user.
2.11.2 Adjust/update menu
The General menu, located below the visual, offers a
set of controls for fine-tuning graphic elements. It includes essential
options for adjusting axes, modifying the main title, customizing
borders, purging unnecessary chart elements, and activating automatic
updating. These features are designed to improve the legibility and
accuracy of data visualizations.
■ Modifying axes: this option lets you reconfigure
the scales of the X and Y axes to better reflect data distribution or
zoom in on a specific part of the graph. Users can define specific
ranges, adjust units, or invert axes for better interpretation of
results.Axis titles can also be edited.
■ Edit main title: this field lets you rename the
graph to better describe the data it represents. The title can be
adjusted to include additional contextual information or to meet
scientific communication requirements.
■ Borders customization: this feature lets you
modify the appearance of the chart’s borders, by adjusting their
thickness, color or style. This can be useful for highlighting certain
parts of the visual, especially in contexts where several graphics are
compared side by side.
■ Figure purging: purging removes superfluous or
obsolete elements from the graph, such as annotations or intermediate
data, ensuring a clear, clean visualization. This is particularly useful
when working with dynamic or large data sets.
■ Automatic update: this option activates the
real-time refresh of the visual display when the underlying data
changes. It is particularly relevant in exploratory analysis or data
flow monitoring contexts, where the evolution of results needs to be
followed instantly.
Once the changes have been configured in the corresponding fields, simply validate to apply the adjustments to the visual.
2.11.3 Changing toolbar position
The visual customization toolbar, located at the top
of the graph by default, can be repositioned vertically to facilitate
access to the various customization options when analyzing data. This
flexibility allows better use of display space, especially when working
with complex graphs or when the user interface requires ergonomic
adjustment.
To reposition the toolbar, follow these steps:
■ Go to the
menu at the bottom of the visual,
■ In the
Orientation section, select the
Vertical option to reposition the toolbar to the left
or right of the chart, according to your preference,
■ Finally,
validate the configuration to apply the change.
This change is particularly useful in cases where the horizontal view
can obstruct data reading, or where a more compact interface is
required. The modification optimizes the layout of tools while
maintaining fluid interaction with graphic elements.
Below is an
illustration of this configuration.
2.11.4 Formatting a visual caption
The caption of a visual plays a crucial role in
understanding the data presented. It helps to identify the different
data series or categories, and its formatting can improve the
readability and aesthetics of the graph. This menu offers several
options for customizing the legend according to the user’s
needs.
■ Legend title
★★Color: you
can change the color of the legend title to make it more visible or to
match the theme of the graphic.
★★Size: adjust the
title’s font size so that it’s proportional to the rest of the visual
and easily readable.
★★Police: choose a font that
matches your project’s graphic charter, facilitating consistent
presentation.
■ Label captions ★★Color: change
the color of captions to improve their visibility against the graphic
background.
★★Size: change the font size of labels
to ensure they are easily legible without cluttering visual space.
★★Police: select an appropriate font to ensure maximum
clarity.
★★Change labels: you can also customize
the text of labels to reflect more meaningful terms or ones suited to
your analysis.
■ Global caption modification
★★Background color: adjust the legend’s background
color so that it blends harmoniously with the graphic design while
making the labels easier to read.
★★Border:
customize the legend’s border by modifying its style, color and
thickness to better delimit the legend from the rest of the graphic.
★★Legend orientation: change the legend orientation
(horizontal or vertical) to optimize space and visual organization,
depending on the amount of information to be displayed.
★★Legend thickness: adjust the thickness of the legend
to ensure it is well-defined and distinct, while maintaining a balanced
aesthetic.
2.11.5 Change chart type
In the Type figures section, users can modify the
type of graph used to represent their data. This functionality is
essential to ensure that the visualization chosen best matches the
characteristics of the data and the analysis objectives.
To change the chart type, the user must follow these simple
steps:
■ Access the Figure types section in the dedicated
menu,
■ Browse the list of available options, which includes graphs
such as: histograms, line graphs, box plots, areas, and many more,
■
Select the desired chart type that will highlight the data appropriately
and customize the necessary options,
■ Validate to
apply the change.
Choosing the right chart type is crucial for effective communication
of analysis results. For example, a histogram is particularly useful for
visualizing the distribution of a continuous variable, while a pie chart
can help represent relative shares in a data set. By adjusting the type
of graph, users can optimize the presentation of their data, making it
easier to interpret and understand.
2.11.6 Advanced formatting
The Advanced Formatting section offers a full range
of options for detailed customization of the appearance and layout of
your graph, enhancing the legibility, aesthetics and visual impact of
the data presented. Here are the available options:
■ Graphic title
★★ Padding: adjust the
inner margins of the title by specifying the padding** (inner
margin) on the left, right, top and bottom. This positions the title
optimally in relation to the other elements of the chart.
★★
Title font: choose the desired font for the title,
which can reinforce the visual consistency of the presentation.
★★
Title size: modify the font size to ensure adequate
visibility and highlight the title.
★★ Title font
color: select a color for the title text, enabling aesthetic
customization and better contrast with the background.
■ Title positioning
★★ Position X
(0-1) and Position Y (1-0): define the
position of the title on the chart, with values between 0 and 1. This
allows it to be placed precisely according to the desired
location.
■ Figure margins
★★ Auto
margin: activate or deactivate auto margin to automatically
adjust margins according to content.
★★ External
margin: define specific margins by adjusting values for left,
right, top and bottom margins, allowing you to fine-tune the space
around the graphic.
■ Global font
★★ Size and
Color: set the size and color of the global font for
the entire chart, ensuring consistency and legibility across all
annotations and legends.
■ Figure dimensions
★★ Width
and Height: specify figure dimensions to suit available
display space and presentation requirements.
■ Figure grid
★★ Lower/upper X and Y
range: adjust X and Y axis limits to define displayed value
ranges.
★★ Z-axis position and Y-axis
position: change the position of the axes to improve
readability and data interpretation.
■ Calendar
★★ Choose the desired calendar type
(e.g. Gregorian, Julian, etc.) to display time data
appropriately.
■ Text and annotations
★★
Visibility: control the visibility of annotations on
the chart, allowing you to adjust what should be displayed to avoid
clutter.
★★ Annotation text: customize annotation
text to provide additional information or contextual
clarification.
Using these advanced formatting options, users can create highly
personalized graphs that effectively communicate analysis results while
meeting the aesthetic standards of their presentation.
3. Documentation menu
THINK-AI’s Documentation menu is divided into three
main sections:
■ About: this section presents the framework for
creating the platform and the specific challenges it addresses,
■
Examples and tutorials: this section includes official
documentation, as well as tutorials and practical examples to help users
learn and better understand the platform’s features,
■
Articles : ici sont publiés des articles de tout genre,
liés au monde des données et de l’intelligence artificielle. This
section features in-depth analyses, case studies and recent trends in
the field.
To access the official THINK-AI documentation, go to
Abstract > Documentation > Examples and
tutorials.
4. Pricing menu
The Pricing menu gives access to the various
subscription offers available on the THINK-AI platform.Four types of
packages are available, tailored to various user profiles:
■Free or Student offer: this option is ideal for
students or novice users wishing to explore the platform’s basic
features at no initial cost,
■ Classic Package:
this package offers a standard set of features, perfectly suited to
regular users or small businesses,
■Premium
Package: this level includes advanced functionalities, designed
for professionals or companies with more advanced needs in terms of
artificial intelligence and data processing,
■Customized
offer: this option enables complete customization of the
subscription, offering a service tailored to the specific needs of each
user or organization.
Each package is distinguished by the range of features available, as
well as by benefits that vary according to the user’s profile.Full
details on pricing and features included are available in this menu to
make it easier to choose the most suitable option.
5.Contact menu
The Contact menu is divided into two main sections:
■ Contact Us: this section provides full company
contact details, including address, telephone number and e-mail address,
to make it easy to get in touch,
■ Customer Account: this menu is subdivided into
three subsections for simplified management of personal
information:
★★ Profile: this section contains all information
relating to the customer’s account, such as name, contact details and
preferences,
★★ Invoicing: allows you to view the
history of invoices and payments associated with the account, ★★
Activities: provides a summary of recent actions
carried out on the platform, including logins, purchases or account
modifications.
These sub-menus provide quick access to essential customer
information and make it easier to manage your account on the THINK-AI
platform.
E.Polls and surveys tab: features
The Surveys tab lets you create and manage questionnaires to be sent
to your respondents. It offers a simple, intuitive interface for
designing surveys by choosing from different types of questions.Whether
for market research, internal surveys or satisfaction assessments, this
solution offers flexibility and robustness to suit the needs of every
user. Here are the key steps to using this feature:
■ Select the question type:
Choose a question
type from the drop-down list in the Field type
menu.This allows you to customize the form of answers (multiple choice,
checkboxes, free text, etc.),
■ Name question:
Use the Field name field to define the title of your
question, which is the text that will appear for each question in the
survey,
■ Define whether field is mandatory:
Activate the Mandatory field option if you want this
question to be completed by all respondents,
■ Add a
question:
To add a new question, click on the Add a
field button. This allows you to enrich your questionnaire
according to your needs,
■ Delete a question:
If you wish to delete a question, simply click on the
Delete button next to the corresponding field,
■
Generate survey : Once all questions have been added
and configured, click on the Generate survey button to
finalize and create the complete questionnaire.
The illustration below shows a survey generated by Think-AI, using
the steps described above. Once the survey has been generated, it can be
emailed, saved,
submitted or printed by clicking on
the corresponding buttons at the bottom of the survey.
F.CEO contact
For further information or advice on using Think-AI’s advanced
features, please contact our CEO :
■ Name of CEO: Dr. Komi NAGBE,PhD. ■ Email: nagbekom@yahoo.fr ■ Phone: (+33) 0763583456
We’d be delighted to help you optimize your data and use Think-AI tools to meet your specific needs.G.Conclusion
This documentation provides a detailed presentation of the processes
involved in data management and survey creation using the Think-AI
platform, integrating artificial intelligence. We have explored the
various stages, from data mining, visualization generation and
personalization, to question configuration and the generation of surveys
ready for distribution.
The platform is constantly evolving to meet market expectations and
offer ever more innovative functionalities, consolidating Think-AI’s
position in the fields of data management and artificial
intelligence.