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MOST: Model-Based Compression with Outlier Storage for Time Series Data
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/spe.2203openalex-snapshot
Decoding billions of integers per second through vectorization
inferredcites →https://doi.org/10.1007/s002360050048openalex-snapshot
The log-structured merge-tree (LSM-tree)
inferredcites →https://doi.org/10.1007/s00422-009-0362-1openalex-snapshot
The identification of critical fluctuations and phase transitions in short term and coarse-grained time series—a method for the real-time monitoring of human change processes
inferredcites →https://doi.org/10.1007/s00778-006-0004-3openalex-snapshot
Efficient query evaluation on probabilistic databases
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Approximate Query Processing: What is New and Where to Go?
inferredcites →https://doi.org/10.1016/j.engappai.2019.08.018openalex-snapshot
Hybrid structures in time series modeling and forecasting: A review
inferredcites →https://doi.org/10.1016/j.ijresmar.2016.11.003openalex-snapshot
The Internet-of-Things: Review and research directions
inferredcites →https://doi.org/10.1016/j.snb.2015.03.028openalex-snapshot
Reservoir computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring
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Innovative Trend Analysis Methodology
inferredcites →https://doi.org/10.1109/cloud.2018.00093openalex-snapshot
Reviewing Cloud Monitoring: Towards Cloud Resource Profiling
inferredcites →https://doi.org/10.1109/icde.1999.754915openalex-snapshot
Efficient time series matching by wavelets
inferredcites →https://doi.org/10.1109/icde.2019.00111openalex-snapshot
Mostly Order Preserving Dictionaries
inferredcites →https://doi.org/10.1109/icde48307.2020.00119openalex-snapshot
Two-Level Data Compression using Machine Learning in Time Series Database
inferredcites →https://doi.org/10.1109/icde51399.2021.00145openalex-snapshot
Optimizing Error-Bounded Lossy Compression for Scientific Data by Dynamic Spline Interpolation
inferredcites →https://doi.org/10.1109/icde53745.2022.00232openalex-snapshot
Improving Prediction-Based Lossy Compression Dramatically via Ratio-Quality Modeling
inferredcites →https://doi.org/10.1109/iceca49313.2020.9297652openalex-snapshot
Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models
inferredcites →https://doi.org/10.1109/iswc.2012.13openalex-snapshot
Introducing a New Benchmarked Dataset for Activity Monitoring
inferredcites →https://doi.org/10.1109/pic.2010.5687485openalex-snapshot
Anomaly detection on time series
inferredcites →https://doi.org/10.1109/ssdbm.2004.51openalex-snapshot
Mining deviants in time series data streams
inferredcites →https://doi.org/10.1109/ssdm.1997.621151openalex-snapshot
Large-sample and deterministic confidence intervals for online aggregation
inferredcites →https://doi.org/10.1109/tbdata.2022.3201176openalex-snapshot
SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors
inferredcites →https://doi.org/10.1109/tbme.2015.2411672openalex-snapshot
Compressed Sensing of Multichannel EEG Signals: The Simultaneous Cosparsity and Low-Rank Optimization
inferredcites →https://doi.org/10.1109/tit.1977.1055714openalex-snapshot
A universal algorithm for sequential data compression
inferredcites →https://doi.org/10.1109/tkde.2008.29openalex-snapshot
Novel Online Methods for Time Series Segmentation
inferredcites →https://doi.org/10.1145/1321440.1321552openalex-snapshot
Detecting distance-based outliers in streams of data
inferredcites →https://doi.org/10.1145/1376616.1376696openalex-snapshot
Querying continuous functions in a database system
inferredcites →https://doi.org/10.1145/191839openalex-snapshot
Proceedings of the 1994 ACM SIGMOD international conference on Management of data
inferredcites →https://doi.org/10.1145/2207676.2208303openalex-snapshot
Instructing people for training gestural interactive systems
inferredcites →https://doi.org/10.1145/2413097.2413148openalex-snapshot
Creating and benchmarking a new dataset for physical activity monitoring
inferredcites →https://doi.org/10.1145/276304.276320openalex-snapshot
Dimensionality reduction for similarity searching in dynamic databases
inferredcites →https://doi.org/10.1145/3183713.3196909openalex-snapshot
The Case for Learned Index Structures
inferredcites →https://doi.org/10.1145/3292500.3330680openalex-snapshot
Time-Series Anomaly Detection Service at Microsoft
inferredcites →https://doi.org/10.1145/3318464.3389711openalex-snapshot
ALEX: An Updatable Adaptive Learned Index
inferredcites →https://doi.org/10.1145/3318464.3389752openalex-snapshot
The Case for a Learned Sorting Algorithm
inferredcites →https://doi.org/10.1145/3444690openalex-snapshot
A Review on Outlier/Anomaly Detection in Time Series Data
inferredcites →https://doi.org/10.1145/3589775openalex-snapshot
Apache IoTDB: A Time Series Database for IoT Applications
inferredcites →https://doi.org/10.1145/602259.602294openalex-snapshot
Accurate estimation of the number of tuples satisfying a condition
inferredcites →https://doi.org/10.1145/882082.882086openalex-snapshot
A symbolic representation of time series, with implications for streaming algorithms
inferredcites →https://doi.org/10.14778/3476249.3476305openalex-snapshot
Decomposed bounded floats for fast compression and queries
inferredcites →https://doi.org/10.1561/1900000004openalex-snapshot
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
inferredcites →https://doi.org/10.1561/1900000052openalex-snapshot
Query Processing on Probabilistic Data: A Survey
inferredcites →https://doi.org/10.1561/9781680833157openalex-snapshot
Query Processing on Probabilistic Data: A Survey
inferredcites →https://doi.org/10.24963/ijcai.2019openalex-snapshot
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-032-18765-9_5openalex-snapshot
Current Systems for Managing Massive High Frequency Time Series
inferred← citeshttps://doi.org/10.1007/s00778-024-00862-zopenalex-snapshot
Flexible grouping of linear segments for highly accurate lossy compression of time series data
inferred← citeshttps://doi.org/10.1109/aic70896.2026.11716790openalex-snapshot
Optimization and Practice of High-Performance Advertising Data Compression Based on LZ4 and ZSTD Algorithm Fusion
inferred← citeshttps://doi.org/10.1109/icde65448.2025.00037openalex-snapshot
A Cost-Effective and Decompression-Transparent Compressor for OLTP-Oriented Databases
inferred← citeshttps://doi.org/10.1109/ispa67752.2025.00100openalex-snapshot
TopcDB: Two-Phase Compactable Lossy Compression for Timeseries Databases
inferred← citeshttps://doi.org/10.1109/jiot.2026.3707225openalex-snapshot
Tcm: A High-Precision Lossy Compression Algorithm for Time Series Data With Flexible Queue-Value Dynamic Grouping
inferred← citeshttps://doi.org/10.1145/3725353openalex-snapshot
Serf : Streaming Error-Bounded Floating-Point Compression
inferred← citeshttps://doi.org/10.1145/3726523openalex-snapshot
Apache IoTDB: A Time Series Database for Large Scale IoT Applications
inferred← citeshttps://doi.org/10.14778/3749646.3749701openalex-snapshot
Beyond Compression: A Comprehensive Evaluation of Lossless Floating-Point Compression
inferred← citeshttps://doi.org/10.2139/ssrn.6330153openalex-snapshot
Tool Wear Prediction Enhanced by Semantic-aware Data Compression
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