Data compression for multidimensional time series data

Abstract

Described herein are computer-implemented methods for compressing sparse multidimensional ordered series data. In particular, these methods and apparatuses for performing them (including software) may be particularly well suited to efficiently compressing spectrographic data.

Description

CROSS REFERENCE TO RELATED APPLICATIONS[0001]This patent application claims priority to U.S. Provisional Patent Application No. 63 / 072,890, filed on Aug. 31, 2020, and titled “DATA COMPRESSION FOR MULTIDIMENSIONAL TIME SERIES DATA,” which is herein incorporated by reference in its entirety.INCORPORATION BY REFERENCE[0002]All publications and patent applications mentioned in this specification are herein incorporated by reference in their entirety to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference.FIELD[0003]Described herein are systems and methods for compressing large multi-dimensional data sets, such as (but not limited to) spectroscopic data in mass spectrometry, microscopy and fluorescence microscopy, and histopathology data.BACKGROUND[0004]In many areas of science and engineering, such as in biology, chemistry, astronomy, physics, geology and object tracking, large quantities of ...

AI Technical Summary

Benefits of technology

The patent is about a new way to compress and transfer large amounts of spectroscopic data, which can be useful in analyzing the composition of things like proteins. The traditional way to compress data involves using methods that limit the fidelity of the original data. However, the methods described in the patent can compress data with high fidelity, allowing for much smaller file sizes. This can save time and money in storing and transferring the data, and it also allows for quicker processing and viewing of the images. The compression methods described in the patent work particularly well with multi-dimensional data, which is typically more difficult to compress. Overall, this new technology can make it easier to handle and analyze large amounts of spectroscopic data.

Problems solved by technology

The proposed methods may result in a substantial decrease in the cost of storing and transferring this type of data, and in a significantly faster processing time, including on-demand processing and viewing of images.

Existing compression methods are not very effective in dealing with large multi-dimensional spectroscopic data.

However, the amount of compression that can be obtained with lossless compression is usually rather limited, typically less than 2 for spectroscopic data, and in some cases the resulting compressed file size may even expand to become larger than the original file, depending on the specific lossless compression method.

However, once loss is allowed, it is often difficult to guarantee the accuracy of the result, due to the subjective nature of the errors.

Large data sets obtained using such sensors often do not include repeating patterns that can be recognized and effectively compressed utilizing existing conventional compression systems.

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