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Das Paper "Efficient Specialized Spreadsheet Parsing for Data Science" zur Präsentation bei DOLAP 2022 Workshop angenommen

Die Forschungsarbeit "Efficient Specialized Spreadsheet Parsing for Data Science" von Felix Henze, Haralampos Gavriilidis, Eleni Tzirita Zacharatou und Volker Markl wurde zur Präsentation bei dem  24. International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP 2022) angenommen. 


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 address this limitation, 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 approach is up to 3x faster while consuming up to 40x less memory than state-of-the-art approaches.