From the post:
Graph databases can be used to analyze data from disparate datasources. In this use-case, three relational databases have been exported to CSV. Each relational export is ingested into its own sharded sub-graph to increase performance and avoid lock contention when merging the datasets. Unique keys overlap the datasources to provide the mechanism to link the subgraphs produced from parsing the CSV. A REST server is used to send the merged graph to a visualization application for analysis.
Cleaning out my pending posts file when I ran this one.
Would be a good comparison case for my topic maps class.
Although I would have to do in installation work on a public facing server and leave the class members to do the analysis/uploading.
Hmmm, perhaps split the class into teams, some of which using this method, some using more traditional record linkage and some using topic maps, all on the same data.
Suggestions on data sets that would highlight the differences? Or result in few differences at all? (I suspect both to be true, depending upon the data sets.)