Decomposed Bounded Floats for Fast Compression and Queries
Chunwei Liu, Hao Jiang, John Paparrizos, Aaron J. Elmore
2021Year
65Citations
30Top-tier citations
Abstract
Modern data-intensive applications often generate large amounts of low precision float data with a limited range of values. Despite the prevalence of such data, there is a lack of an effective solution to ingest, store, and analyze bounded, low-precision, numeric data. To address this gap, we propose Buff, a new compression technique that uses a decomposed columnar storage and encoding methods to provide effective compression, fast ingestion, and high-speed in-situ adaptive query operators with SIMD support.
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Cited by top-tier papers30
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- Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time SeriesEmmanouil Sylligardos, Paul Boniol, John Paparrizos, Panos E. Trahanias et al.VLDB 2023 · 40 citations
- Fast Adaptive Similarity Search through Variance-Aware QuantizationJohn Paparrizos, Ikraduya Edian, Chunwei Liu, Aaron J. Elmore et al.ICDE 2022 · 34 citations
- FCBench: Cross-Domain Benchmarking of Lossless Compression for Floating-point DataXinyu Chen, Jiannan Tian, Ian Beaver, Cynthia Freeman et al.VLDB 2024 · 24 citations
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- Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud ProviderMohammad Shahrad, Rodrigo Fonseca, Iñigo Goiri, Gohar Irfan Chaudhry et al.USENIX ATC 2020 · 946 citations
- SAND: Streaming Subsequence Anomaly DetectionPaul Boniol, John Paparrizos, Themis Palpanas, Michael J. FranklinVLDB 2021 · 128 citations
- Debunking Four Long-Standing Misconceptions of Time-Series Distance MeasuresJohn Paparrizos, Chunwei Liu, Aaron J. Elmore, Michael J. FranklinSIGMOD 2020 · 56 citations
- PIDS: Attribute Decomposition for Improved Compression and Query Performance in Columnar StorageHao Jiang, Chunwei Liu, Qi Jin, John Paparrizos et al.VLDB 2020
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