Learned Selection Strategy for Lightweight Integer Compression Algorithms

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Data compression has recently experienced a revival in the domain of in-memory column stores. In this field, a large corpus of lightweight integer compression algorithms plays a dominant role since all columns are typically encoded as sequences of integer values. Unfortunately, there is no single-best integer compression algorithm and the best algorithm depends on data and hardware properties. For this reason, selecting the best-fitting integer compression algorithm becomes more important and is an interesting tuning knob for optimization. However, traditional selection strategies require a profound knowledge of the (de-)compression algorithms for decision-making. This limits the broad applicability of the selection strategies. To counteract this, we propose a novel learned selection strategy by considering integer compression algorithms as independent black boxes. This black-box approach ensures broad applicability and requires machine learning-based methods to model the required knowledge for decision-making. Most importantly, we show that a local approach, where every algorithm is modeled individually, plays a crucial role. Moreover, our learned selection strategy is generalized by user-data-independence. Finally, we evaluate our approach and compare our approach against existing selection strategies to show the benefits of our learned selection strategy.


Original languageEnglish
Title of host publication26th International Conference on Extending Database Technology (EDBT 2023)
Number of pages13
Publication statusPublished - 28 Mar 2023

External IDs

dblp conf/edbt/WoltmannDHHL23
Scopus 85165055743
ORCID /0000-0001-8107-2775/work/142253565


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