  Spreadsheets are one of the most popular data analysis tools, wherein users
can express computation as formulae alongside data. The ensuing dependencies
are tracked as formula graphs. Efficiently querying and maintaining these
formula graphs is critical for interactivity across multiple settings.
Unfortunately, formula graphs are often large and complex such that querying
and maintaining them is time-consuming, reducing interactivity. We propose
TACO, a framework for efficiently compressing formula graphs, thereby reducing
the time for querying and maintenance. The efficiency of TACO stems from a key
spreadsheet property: tabular locality, which means that cells close to each
other are likely to have similar formula structures. We leverage four such
tabular locality-based patterns and develop algorithms for compressing formula
graphs using these patterns, directly querying the compressed graph without
decompression, and incrementally maintaining the graph during updates. We
integrate TACO into an open-source spreadsheet system and show that TACO can
significantly reduce formula graph sizes. For querying formula graphs, the
speedups of TACO over a baseline implemented in our framework and a commercial
spreadsheet system are up to 34,972x and 632x, respectively.
