2025/04/15 by Sebastian Baunsgaard, Baunsgaard, Sebastian, Matthias Böehm +1
Computer Science · #Advanced Data Storage Technologies #Advanced Database Systems and Queries #Databases (cs.DB) #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel #Parallel Computing and Optimization Techniques #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2504.11067
openalex publication_date 2025/04/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Data-centric ML pipelines extend traditional machine learning (ML) pipelines -- of feature transformations and ML model training -- by outer loops for data cleaning, augmentation, and feature engineering to create high-quality input data. Existing lossless matrix compression applies lightweight compression schemes to numeric matrices and performs linear algebra operations such as matrix-vector multiplications directly on the compressed representation but struggles to efficiently rediscover structural data redundancy. Compressed operations are effective at fitting data in available memory, reducing I/O across the storage-memory-cache hierarchy, and improving instruction parallelism. The applied data cleaning, augmentation, and feature transformations provide a rich source of information about data characteristics such as distinct items, column sparsity, and column correlations. In this paper, we introduce BWARE -- an extension of AWARE for workload-aware lossless matrix compression -- that pushes compression through feature transformations and engineering to leverage information about structural transformations. Besides compressed feature transformations, we introduce a novel technique for lightweight morphing of a compressed representation into workload-optimized compressed representations without decompression. BWARE shows substantial end-to-end runtime improvements, reducing the execution time for training data-centric ML pipelines from days to hours.