Data Profiling in Property Graph Databases

In 2020, as part of the master’s program I am currently pursuing, we published a paper on data profiling in graph databases in the ACM’s *Journal of Data and Information Quality*.

The process of writing and revising the paper took us about a year and a half, during which we received several suggestions for changes from the reviewers that helped significantly improve the work.

The aim of the article is to review and organize different techniques and methods that can be applied to data profiling on a graph database.

This is the first paper I’ve published, so it was a nice milestone during such an unusual year.

Here is the abstract for more details:

Property graph databases are increasingly being used in the industry as a powerful and flexible way to model real-world scenarios. This flexibility presents a major challenge for profiling tasks, as they must be adapted to these new models while taking advantage of the unique characteristics of property graphs. This article proposes a set of data profiling tasks by integrating existing methods and techniques, along with a taxonomy to classify them. Additionally, an application pipeline is provided, and a formal specification of some tasks is defined.

The link to the publication is as follows: https://dl.acm.org/doi/fullHtml/10.1145/3409473

Currently, as part of my master’s thesis, I am researching some of the techniques and methods mentioned in the article, particularly in the area of functional dependencies.

Until next time!