**6. Conclusion and future work**

#### **6.1 Conclusion**

The demand for a good visual analytics tool is increasing due to the need to cater to complex data which is constantly increasing. A complete visual analytics solution should cover all the procedures in the conventional visual analytics pipeline. These procedures, starting from the basic data pre-processing to clean the data, allow the user to apply machine learning methods in order to transform the data, and extract hidden correlations in the data. Later, the transformed data can be visualized to find trends and patterns that can be crucial in extracting knowledge from the data.

While there are various tools available in the market that allow the users to either create dynamic, interactive visualizations or implement machine learning models, none of the tools cover the complete visual analytics pipeline with their built-in features. These tools rely on third-party extensions that can be quite tiresome to configure and integrate. Additionally, integrating these third-party extensions may make the tools quite heavy and unstable. This project discusses the visual analytics pipeline and presents a solution that implements the complete visual analytics pipeline in order to fill this void. In this project, a web-based tool called ViDAS was developed using the "Human-centered approach" that packages the complete visual analytics pipeline integrated into it. ViDAS was developed with a number of requirements in mind that are not available in the tools currently in the market. These requirements included the tool being light-weight so that it can be deployed on a web server, dealing with big data, allowing machine learning methods and custom scripts, and visualizing the data in an interactive way.

Initially, the context of use for the tool and requirements were gathered in a series of workshops and meetings. ViDAS allows it's users to upload raw data and apply initial data filtering to only process the required attributes. These attributes can later be used to either create interactive visualizations or apply custom analysis. If the custom analysis is applied, the resultant attributes can be exported to the 'Data Analysis' tab for visualization of those ML models. The chart creation process of ViDAS comprises of a drag-and-drop workflow and is very intuitive. Furthermore, ViDAS packages a chart recommendation functionality that suggests a number of charts according to the fields selected. By comparing the feedback of the tools used in the requirement gathering process to the feedback of ViDAS in its evaluation, an improvement was seen in the overall tool experience.
