Extract-Transform-Load (ETL) workflows are data centric workflows responsible for transferring, cleaning, and loading data from their respective sources to the warehouse. In this paper, we build upon existing graph-based modeling techniques that treat ETL workflows as graphs by (a) extending the activity semantics to incorporate negation, aggregation and self-joins, (b) complementing querying semantics with insertions, deletions and updates, and (c) transforming the graph to allow zoom-in/out at multiple levels of abstraction (i.e., passing from the detailed description of the graph at the attribute level to more compact variants involving programs, relations and queries and vice-versa).
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