Chimp: efficient lossless floating point compression for time series databases
P Liakos, K Papakonstantinopoulou… - Proceedings of the VLDB …, 2022 - dl.acm.org
Applications in diverse domains such as astronomy, economics and industrial monitoring,
increasingly press the need for analyzing massive collections of time series data. The sheer …
increasingly press the need for analyzing massive collections of time series data. The sheer …
Mdz: An efficient error-bounded lossy compressor for molecular dynamics
Molecular dynamics (MD) has been widely used in today's scientific research across
multiple domains including materials science, biochemistry, biophysics, and structural …
multiple domains including materials science, biochemistry, biophysics, and structural …
Adaedge: A dynamic compression selection framework for resource constrained devices
With the Internet of Things (IoT), a vast number of connected devices generate significant
data, necessitating efficient compression techniques to manage storage costs and enhance …
data, necessitating efficient compression techniques to manage storage costs and enhance …
Elf: Erasing-based lossless floating-point compression
There are a prohibitively large number of floating-point time series data generated at an
unprecedentedly high rate. An efficient, compact and lossless compression for time series …
unprecedentedly high rate. An efficient, compact and lossless compression for time series …
Erasing-based lossless compression method for streaming floating-point time series
There are a prohibitively large number of floating-point time series data generated at an
unprecedentedly high rate. An efficient, compact and lossless compression for time series …
unprecedentedly high rate. An efficient, compact and lossless compression for time series …
ModelarDB: integrated model-based management of time series from edge to cloud
To ensure critical infrastructure is operating as expected, high-quality sensors are
increasingly installed. However, due to the enormous amounts of high-frequency time series …
increasingly installed. However, due to the enormous amounts of high-frequency time series …
Machine learning platform for extreme scale computing on compressed IoT data
With the lowering costs of sensors, high-volume and high-velocity data are increasingly
being generated and analyzed, especially in IoT domains like energy and smart homes …
being generated and analyzed, especially in IoT domains like energy and smart homes …
Visualization-aware Time Series Min-Max Caching with Error Bound Guarantees
S Maroulis, V Stamatopoulos… - Proceedings of the …, 2024 - dl.acm.org
This paper addresses the challenges in interactive visual exploration of large multi-variate
time series data. Traditional data reduction techniques may improve latency but can distort …
time series data. Traditional data reduction techniques may improve latency but can distort …
The danish national energy data lake: Requirements, technical architecture, and tool selection
HB Hamadou, TB Pedersen… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
Renewable Energy Sources such as wind and solar do not emit CO 2 but their production
vary considerably depending on time and weather. Thus, it is important to use the flexibility …
vary considerably depending on time and weather. Thus, it is important to use the flexibility …
Holistic analytics of sensor data from renewable energy sources: a vision paper
Abstract Modern Renewable Energy System (RES) installations, eg, wind turbines, produce
petabytes of high-frequency time series. State-of-the-art systems cannot cope with such …
petabytes of high-frequency time series. State-of-the-art systems cannot cope with such …