Nafisa Anzum
Daniel Vogel
ABSTRACT
Existing data stores of enterprises are full of connected data and users are increasingly finding value in performing graph querying, analytics and visualization on this data. This process involves a labor-intensive ETL pipeline, where users write scripts to extract graphs from data stored in legacy stores, often an RDBMS, and import these graphs into a graph-specific software. We demonstrate GraphWrangler, a system that allows users to connect to an RDBMS and within a few clicks extract graphs out of their tabular data, visualize and explore these graphs, and automatically generate scripts for their ETL pipelines. GraphWrangler adopts the predictive interaction framework and internally uses a data transformation language that is a limited subset of SQL. Our demonstration video can be found here: https://youtu.be/k92Qk6vuIsU
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Index Terms
- GraphWrangler: An Interactive Graph View on Relational Data
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