InetSoft Webinar: Data Visualizations Are Quick at Solving a Problem

This is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of "How Data Discovery Software Uncovers Stories in the Data." The speaker is Abhishek Gupta, Product Manager at InetSoft.

Statistical Views

Another statistical view shows you metrics really well with means and standard deviations. A multiscape is a 3D view histogram. A data constellation is a view that shows affinities and clusters. You might use it in social networking to see who’s related to whom. It also can be used in a network topology that may be used to see how network traffic flows and so forth.

But the point of that was there are a set of charts that are able to help you see your data in a way that helps the data tell the story. Quickly a third example is with an airline network with overbooked flights. The data consists of single flights. You wouldn’t just want to sort a list to see the worst flights. What you care about is how groups of them do.

So here’s an example of a visualization that takes a major carrier’s North American flight Network, and the pillars that are on the airports, the bigger the pillar the more over sold are its flights, and the darker the color the more each flight is oversold. So you look at this and something jump out with a lot color and with the higher columns is the northeast. This is where there are more overbooked flights.

Some of the flights segments, like Boston to Miami is a problem. And Miami to LA is a problem. So it’s not an individual flight. It’s the pattern. So in this case, you can clearly see that Boston’s having a problem. You can click on Boston, and you can see which specifics flights in Boston having the most problems. And you can go the detail page and actually export the list of flights which you want to look at and take action.

Maybe the problem is in that condition, if a storm comes through, you can have a problem with a lot stranded passengers. That’s an example of where you can picture how a long list of delayed flights would be really hard for people to pay attention to and focus on a pattern. And in this case seeing the pattern of the northeast, you might make different decisions after seeing the worst flight which actually were some of those going to Miami to Los Angeles and so forth, you might change what you do by seeing the data in a way that tells a story.

With the couple of minutes we have left we’re going to quickly look at predictive analytics, which complements the other visualization technology. Here it’s using mathematical tools to determine patterns in one set of data in order to predict behavior in another set of data. It integrates really well with data visualization. Let’s take a look.

So here the process is you select the target to compare a base population. You look at explanatory fields which can be in the main table, or because we have all the data in-memory you can calculate them in the other tables and put them in the main table. So there may be 20 or 30 core tables of contacts, and the main one is the people where you’ve got information on degrees and employment and activities, you can create factors on those tables and bring them back to the main table and use them in a regression.

Using Data Visualization in Sound and Music Cognition Research

Sound and music cognition research explores how humans perceive, process, and respond to auditory stimuli. As datasets grow in complexity—ranging from neural recordings to emotional response surveys—data visualization becomes essential for uncovering patterns and communicating insights.

Visualizing Auditory Features

Researchers often analyze features such as pitch, timbre, rhythm, and harmonic structure. Spectrograms and waveform plots are foundational tools for visualizing audio signals over time. Chromagrams and tonal centroid plots help map harmonic progressions and key changes, offering insights into musical structure and listener expectations.

Mapping Cognitive and Emotional Responses

To understand how music affects cognition and emotion, researchers use heatmaps, violin plots, and multidimensional scaling to visualize survey data, EEG signals, and physiological metrics. These visuals can reveal how different genres or tempos influence attention, memory, and mood across populations.

Cross-Modal Analysis

Music cognition often intersects with visual, linguistic, and motor domains. Sankey diagrams and network graphs can illustrate connections between auditory stimuli and other sensory modalities. For example, linking rhythmic patterns to motor responses or mapping lyrical content to semantic networks.

Interactive Dashboards for Experimental Design

Interactive dashboards allow researchers to explore participant data in real time, filter by variables such as age or musical training, and adjust experimental parameters dynamically. These tools enhance reproducibility and facilitate hypothesis generation.

Conclusion

Data visualization transforms sound and music cognition research by making abstract auditory phenomena tangible. Through intuitive visuals, researchers can decode the intricate relationship between sound and the human mind, paving the way for advances in therapy, education, and artificial intelligence.

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