Decomposed Bounded Floats for Fast Compression and Queries
Chunwei Liu, Hao Jiang, John Paparrizos, Aaron J. Elmore
2021年份
65被引次数
30顶会引用
摘要
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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引用它的顶会 Paper30
- Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly DetectionJohn Paparrizos, Paul Boniol, Themis Palpanas, Ruey S. Tsay 等VLDB 2022 · 被引用 171 次
- Elf: Erasing-based Lossless Floating-Point CompressionRuiyuan Li, Zheng Li, Yi Wu, Chao Chen 等VLDB 2023 · 被引用 44 次
- Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time SeriesEmmanouil Sylligardos, Paul Boniol, John Paparrizos, Panos E. Trahanias 等VLDB 2023 · 被引用 40 次
- Fast Adaptive Similarity Search through Variance-Aware QuantizationJohn Paparrizos, Ikraduya Edian, Chunwei Liu, Aaron J. Elmore 等ICDE 2022 · 被引用 34 次
- FCBench: Cross-Domain Benchmarking of Lossless Compression for Floating-point DataXinyu Chen, Jiannan Tian, Ian Beaver, Cynthia Freeman 等VLDB 2024 · 被引用 24 次
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- SAND: Streaming Subsequence Anomaly DetectionPaul Boniol, John Paparrizos, Themis Palpanas, Michael J. FranklinVLDB 2021 · 被引用 128 次
- Debunking Four Long-Standing Misconceptions of Time-Series Distance MeasuresJohn Paparrizos, Chunwei Liu, Aaron J. Elmore, Michael J. FranklinSIGMOD 2020 · 被引用 56 次
- PIDS: Attribute Decomposition for Improved Compression and Query Performance in Columnar StorageHao Jiang, Chunwei Liu, Qi Jin, John Paparrizos 等VLDB 2020
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