A multi-dimensional extension of the Lightweight Temporal Compression method
| Autor: | Hosein Nourani, Bo Li, O. Sarbishei, Tristan Glatard |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Data stream mining Computer science Computer Science - Information Theory Information Theory (cs.IT) FOS: Physical sciences 020206 networking & telecommunications Data compression ratio 02 engineering and technology Lossy compression 01 natural sciences Measure (mathematics) Computational science Dimension (vector space) Physics - Data Analysis Statistics and Probability Compression (functional analysis) 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 010303 astronomy & astrophysics Data Analysis Statistics and Probability (physics.data-an) |
| Zdroj: | IEEE BigData |
| DOI: | 10.48550/arxiv.1811.09930 |
| Popis: | Lightweight Temporal Compression (LTC) is among the lossy stream compression methods that provide the highest compression rate for the lowest CPU and memory consumption. As such, it is well suited to compress data streams in energy-constrained systems such as connected objects. The current formulation of LTC, however, is one-dimensional while data acquired in connected objects is often multi-dimensional: for instance, accelerometers and gyroscopes usually measure variables along 3 directions. In this paper, we investigate the extension of LTC to higher dimensions. First, we provide a formulation of the algorithm in an arbitrary vectorial space of dimension n. Then, we implement the algorithm for the infinity and Euclidean norms, in spaces of dimension 2D+t and 3D+t. We evaluate our implementation on 3D acceleration streams of human activities. Results show that the 3D implementation of LTC can save up to 20% in energy consumption for low-paced activities, with a memory usage of about 100 B. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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