Process modeling languages have been used extensively in designing logical tasks that use resources, and in explaining how the process evolves from beginning to end. They are essential in understanding how activities will work.

My experience with using these languages is with data-flow diagrams (DFDs) for parallel computing algorithms, and with understanding how software tasks were being divided and communicated across. That is why I chose to review this paper.

As this very good, extensively researched paper shows, the authors clearly are experts in what they are describing. They discuss using process management for assessing the quality of data in various publications. The paper provides a good overview of current research in the field as well.

There are some places where the paper is very strong. The authors discuss a clear contrast between process-level and organization-level data processing. They describe the various processes and methodologies used, and define their limitations for information quality management. In the end, the paper highlights an additional need for further research in specific modeling languages.

It could be because I am not a data scientist, but I found the paper a bit hard to follow. I had to keep reviewing previous sections to understand the methodology being used and how experiments were being conducted. However, once I understood, I found the approach quite impressive. There are some interesting concepts here, which all computational learning approaches can apply in assessing quality in various tools we are building; therefore, the potential of this paper is not limited to information quality management.