Efficient and Compact Spreadsheet Formula Graphs

TL;DR

TACO is a framework that compresses spreadsheet formula graphs by exploiting tabular locality, enabling faster querying and maintenance, which significantly improves interactivity in spreadsheet systems.

Contribution

We introduce TACO, a novel compression framework for formula graphs that leverages tabular locality patterns to enhance efficiency and support incremental updates.

Findings

TACO reduces formula graph sizes significantly.

Querying speedups of up to 34,972x over baseline.

Effective incremental maintenance during updates.

Abstract

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.