What Polynomial Regression Reveals About Scientific Data Compression.
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| Title: | What Polynomial Regression Reveals About Scientific Data Compression. |
|---|---|
| Authors: | McArdle, James1 (AUTHOR) jedishrfu@txstate.edu, Burtscher, Martin1 (AUTHOR) burtscher@txstate.edu |
| Source: | IEEE Pulse. Jan-Feb2026, Vol. 17 Issue 1, p57-59. 3p. |
| Subjects: | Data compression, Lossy data compression, Floating-point arithmetic, Curve fitting, Data structures, Regression analysis, Data analysis |
| Abstract: | Scientific instruments and large-scale simulations produce vast amounts of floating-point data, posing challenges for storage, transfer, and analysis. Modern lossy compression reduces data size within error limits but often obscures local data behavior. Consequently, compression is seen mainly as a tool for size reduction rather than a means of learning about the data. Polynomial regression offers another option. Fitting simple mathematical curves to small regions of data can both reduce data size and highlight features such as smooth trends, abrupt changes, and noisy regions. In this work, we examine piecewise polynomial regression as a structure-aware form of compression. Our goal is less about outperforming other compressors and more about showing how locally fitted models can expose the underlying structure of the data. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Pulse is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193011942 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: What Polynomial Regression Reveals About Scientific Data Compression. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22McArdle%2C+James%22">McArdle, James</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jedishrfu@txstate.edu</i><br /><searchLink fieldCode="AR" term="%22Burtscher%2C+Martin%22">Burtscher, Martin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> burtscher@txstate.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Pulse%22">IEEE Pulse</searchLink>. Jan-Feb2026, Vol. 17 Issue 1, p57-59. 3p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Data+compression%22">Data compression</searchLink><br /><searchLink fieldCode="DE" term="%22Lossy+data+compression%22">Lossy data compression</searchLink><br /><searchLink fieldCode="DE" term="%22Floating-point+arithmetic%22">Floating-point arithmetic</searchLink><br /><searchLink fieldCode="DE" term="%22Curve+fitting%22">Curve fitting</searchLink><br /><searchLink fieldCode="DE" term="%22Data+structures%22">Data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Scientific instruments and large-scale simulations produce vast amounts of floating-point data, posing challenges for storage, transfer, and analysis. Modern lossy compression reduces data size within error limits but often obscures local data behavior. Consequently, compression is seen mainly as a tool for size reduction rather than a means of learning about the data. Polynomial regression offers another option. Fitting simple mathematical curves to small regions of data can both reduce data size and highlight features such as smooth trends, abrupt changes, and noisy regions. In this work, we examine piecewise polynomial regression as a structure-aware form of compression. Our goal is less about outperforming other compressors and more about showing how locally fitted models can expose the underlying structure of the data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Pulse is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/MPULS.2026.3659277 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 3 StartPage: 57 Subjects: – SubjectFull: Data compression Type: general – SubjectFull: Lossy data compression Type: general – SubjectFull: Floating-point arithmetic Type: general – SubjectFull: Curve fitting Type: general – SubjectFull: Data structures Type: general – SubjectFull: Regression analysis Type: general – SubjectFull: Data analysis Type: general Titles: – TitleFull: What Polynomial Regression Reveals About Scientific Data Compression. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: McArdle, James – PersonEntity: Name: NameFull: Burtscher, Martin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan-Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 21542287 Numbering: – Type: volume Value: 17 – Type: issue Value: 1 Titles: – TitleFull: IEEE Pulse Type: main |
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