Security News > 2020 > August > Researchers develop a process to categorize massive datasets, making data more accessible

A computer science professor at The University of Texas at Arlington is working with researchers to develop a process by which data points in multiple graph layers of massive datasets can be connected in a way that is both highly scalable and will allow analysts to look at it in greater depth.

The analysis results preserve semantics and structure and make it easy to create visualizations of the data and results, enabling analysts to picture how the layers of data fit together with greater ease.

"It's not a new model, but our approach to its analysis is new. The results are customizable to the needs of the researcher and are more actionable, tangible and easier to understand. Down the road, we'd like to try fusing different types of data, such as video and audio, with the structured data."

"Big data analysis is difficult because of the immense amount of information involved in each dataset and the complex embedded relationships," said Hong Jiang, chair of UTA's Computer Science and Engineering Department.

"The ability to categorize massive datasets efficiently so the data becomes useful and accessible is very important, and Dr. Chakravarthy and his colleagues have applied a creative framework to an existing model or representation that will yield better, deeper results."

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