Sampling for Scientific Data Analysis and Reduction

Biswas, Ayan; Dutta, Soumya; Turton, Terece L.; Ahrens, James

doi:10.1007/978-3-030-81627-8_2

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Lossy Data Reduction Algorithms1

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2024

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“…Simple Random Sampling is a data reduction technique that selects a subset of data points from a larger population to create a representative sample that preserves the essential characteristics of the entire dataset [40]. By working with a fraction of the original data, this algorithm allows for more efficient analysis, reduced computational requirements, and faster processing times.…”

Section: Lossy Data Reduction Algorithmsmentioning

confidence: 99%

Analyzing Data Reduction Techniques: An Experimental Perspective

Fernandes,

Carvalho,

Pereira

et al. 2024

Applied Sciences

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The exponential growth in data generation has become a ubiquitous phenomenon in today’s rapidly growing digital technology. Technological advances and the number of connected devices are the main drivers of this expansion. However, the exponential growth of data presents challenges across different architectures, particularly in terms of inefficient energy consumption, suboptimal bandwidth utilization, and the rapid increase in data stored in cloud environments. Therefore, data reduction techniques are crucial to reduce the amount of data transferred and stored. This paper provides a comprehensive review of various data reduction techniques and introduces a taxonomy to classify these methods based on the type of data loss. The experiments conducted in this study include distinct data types, assessing the performance and applicability of these techniques across different datasets.

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Section: Lossy Data Reduction Algorithmsmentioning

confidence: 99%