*1.2.1 Visual analytics*

Visual analytics is a human-centric process that combines techniques from graphics, visualization, interaction, data analysis, and data mining to support reasoning and sense-making for complex problems and extract relevant information from the raw data. While simple visualization techniques can be applied to the simulation data to investigate different parameters, find patterns, and visualize dependencies, data mining and machine learning helps with examining the data further through techniques such as forecasting, clustering, regression, etc. Visual Analytics is comprised of two parts; data analytics and data visualization. These two approaches co-exist and support the visual analytics process to understand complex data and find patterns in it.

Furthermore, visual analytics goes a step further and includes the approach of human-in-the-loop [6]. This approach combines the perceptual capabilities of a human mind along with the interactive data visualizations to apply visual analytics. Visual analytics is not a tool, but a human-centric process that aims at integrating human perception in to the visual data exploration process. It requires the specialist to first understand the data and its context. After the data is prepared, interactive visualizations can be used to find patterns and extract useful information from the data. An ideal visual analytics solution should require little to no coding, allow the user to combine from multiple sources, offer easy-to-customize interactive visualizations, include the feature to drill down the data at any level of detail, and combine multiple views to get an overall understanding of the data.

Currently, there is a lot of work being done in academia and industry towards visual analytics solutions to assist in the sense-making of the data [7]. There are a number of commercial business intelligence solutions that specialize in data discovery such as Tableau [8], Qlik Sense [9], Power BI [10], etc. Additionally, a number of data mining tools are available such as KNIME [11], RapidMiner [12], Orange [13], Weka [14], KEEL [15], etc. that focus on applying machine learning models and provide visualizations to help understand the raw data and find a pattern in it. However, there is a lack of tools that perform both data discovery and apply machine learning models.
