#data reduction

Introduction Training large AI models demands vast amounts of data – a challenge that significantly impacts cost, efficiency, and accessibility. Google Research has tackled this problem head-on, achieving a staggering 10,000x reduction in training data while maintaining high-fidelity results. This breakthrough hinges on a novel approach: generating synthetic training data using high-fidelity…

As we know, Machine Learning, is a long and energy-intensive process, requiring expensive compuational hardware and large amounts of training data. In a recent blog, we have shown how QCM-powered Artificial Intuition by Ontonix, is able to accelerate Machine Learning by eliminating dimensions, or entire training vectors, if their information content is negligible. The above can be accomplished…

Data reduction techniques refer to various methods and approaches used to reduce the size, complexity, and redundancy of data while preserving its essential information. These techniques are applied in diverse fields such as data storage, data analysis, machine learning, and data transmission. The primary goal of data reduction is to optimize storage resources, improve computational efficiency, enhance data processing speed, and facilitate easier data management. 

Here are some common data reduction techniques: 

1. Data Compression: Data compression involves encoding data in a more compact form to reduce the number of bits required for storage or transmission. Compression can be either lossless or lossy. Lossless compression techniques ensure that the original data can be perfectly reconstructed from the compressed version, while lossy compression sacrifices some details to achieve higher compression ratios. Popular compression algorithms include ZIP, GZIP, and LZW (used in GIF images). 

2. Deduplication: Deduplication, also known as data deduplication or duplicate data elimination, involves identifying and removing duplicate or redundant data within a dataset or storage system. By storing only a single instance of each unique piece of data and referencing it elsewhere, deduplication reduces storage space requirements and improves data efficiency. Deduplication is commonly used in backup systems, file storage systems, and cloud storage. 

3. Dimensionality Reduction: Dimensionality reduction techniques aim to reduce the number of variables or features in a dataset while preserving the important characteristics and minimizing information loss. This is particularly useful in machine learning and data analysis tasks where high-dimensional datasets can lead to computational inefficiencies and the curse of dimensionality. Techniques like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and t-SNE (t-Distributed Stochastic Neighbor Embedding) are commonly used for dimensionality reduction. 

4. Sampling: Sampling involves selecting a subset of data points from a larger dataset for analysis or modeling purposes. Instead of using the entire dataset, a representative sample can provide insights and results that approximate those obtained from the complete dataset. Sampling reduces computational and storage requirements, speeds up analysis, and can be particularly useful when dealing with large datasets. 

5. Data Aggregation: Aggregation involves combining multiple data points or records into a single representative value or summary. This is commonly used in data summarization, where large datasets are condensed into smaller, more manageable representations. Aggregation techniques include calculating averages, sums, maximums, minimums, or other statistical measures for groups of data points. 

6. Filtering: Filtering involves removing unnecessary or irrelevant data from a dataset based on specific criteria or conditions. This can be done to remove noise, outliers, or data points that do not meet certain requirements. Filtering helps improve data quality, remove unwanted information, and focus on the most relevant data for analysis or processing. 

Data mining is an interdisciplinary subfield of computer science that analyzes data from various perspectives and summarizes it into useful information. In other words, data mining is a powerful technology used in the extraction of hidden information from large databases. Data mining assignments give prominence to various aspects of the said discipline.

In addition, RapidMiner is the most popular and widespread open-source data mining solution in the world. It is a software tool applied for simple statistical evaluations such as regression, correlation analysis, clustering and classification of analytical procedures, parameter optimization, and dimension reduction.

New @georgeacrump Blog: Overcoming All-Flash Array Post Implementation Problems

Storage Switzerland suggested a few years ago that IT professionals look for ways to get as much as 10 years out of their flash arrays. The problem was, as we cited at that time, most storage hardware manufacturers counted on a three to four year upgrade cycle. The storage refresh cycle is now driven by vendor programs than an actual need to upgrade the technology. In addition the way storage…

With regards to data diminishment techniques for virtual servers, both deduplication and pressure have their place. Pressure for the most part works at the record level, while deduplication tends to work at the square level.

The pressure is most likely most appropriate for use on document servers that contain data that is once in a while got to, for example, chronicled data. In view of the way the pressure work, pack all for the most part is impossible.

Another basic use for pressure in a virtualized situation is pressure NTFS document framework, which a few directors use to decrease the impression of the data in the hidden physical storage volume. Yet, NTFS pressure is an acquired characteristic that is beginning to drop out of style, since it expends CPU cycles. In that capacity, pressure is a poor decision for use in virtual machines (VM) running escalated workloads on CPU. All the more imperatively, a few Windows Server elements, for example, ceaseless accessibility, are not perfect with NTFS pressure.

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