  Spreadsheets are widely used for data exploration. Since spreadsheet systems
have limited capabilities, users often need to load spreadsheets to other data
science environments to perform advanced analytics. However, current approaches
for spreadsheet loading suffer from either high runtime or memory usage, which
hinders data exploration on commodity systems. To make spreasheet loading
practical on commodity systems, we introduce a novel parser that minimizes
memory usage by tightly coupling decompression and parsing. Furthermore, to
reduce the runtime, we introduce optimized spreadsheet-specific parsing
routines and employ parallelism. To evaluate our approach, we implement a
prototype for loading Excel spreadsheets into R environments. Our evaluation
shows that our novel approach is up to 3x faster while consuming up to 40x less
memory than state-of-the-art approaches.
