A Logarithmic Compression Method for Magnitude-Rich Data: The LPPIE Approach
This study introduces Logarithmic Positional Partition Interval Encoding (LPPIE), a novel lossless compression methodology employing iterative logarithmic transformations to drastically reduce data size. While conventional dictionary-based algorithms rely on repeated sequences, LPPIE translates nume...
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MDPI AG
2025-07-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/13/7/278 |
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| author | Vasileios Alevizos Zongliang Yue Sabrina Edralin Clark Xu Nikitas Gerolimos George A. Papakostas |
| author_facet | Vasileios Alevizos Zongliang Yue Sabrina Edralin Clark Xu Nikitas Gerolimos George A. Papakostas |
| author_sort | Vasileios Alevizos |
| collection | DOAJ |
| description | This study introduces Logarithmic Positional Partition Interval Encoding (LPPIE), a novel lossless compression methodology employing iterative logarithmic transformations to drastically reduce data size. While conventional dictionary-based algorithms rely on repeated sequences, LPPIE translates numeric data sequences into highly compact logarithmic representations. This achieves significant reduction in data size, especially on large integer datasets. Experimental comparisons with established compression methods—such as ZIP, Brotli, and Zstandard—demonstrate LPPIE’s exceptional effectiveness, attaining compression ratios nearly 13 times superior to established methods. However, these substantial storage savings come with elevated computational overhead due to LPPIE’s complex numerical operations. The method’s robustness across diverse datasets and minimal scalability limitations underscore its potential for specialized archival scenarios where data fidelity is paramount and processing latency is tolerable. Future enhancements, such as GPU-accelerated computations and hybrid entropy encoding integration, are proposed to further optimize performance and broaden LPPIE’s applicability. Overall, LPPIE offers a compelling alternative in lossless data compression, substantially redefining efficiency boundaries in high-volume numeric data storage. |
| format | Article |
| id | doaj-art-68bd1decca654fc792facacceacf07ac |
| institution | DOAJ |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-68bd1decca654fc792facacceacf07ac2025-08-20T02:47:21ZengMDPI AGTechnologies2227-70802025-07-0113727810.3390/technologies13070278A Logarithmic Compression Method for Magnitude-Rich Data: The LPPIE ApproachVasileios Alevizos0Zongliang Yue1Sabrina Edralin2Clark Xu3Nikitas Gerolimos4George A. Papakostas5Karolinska Institutet, 17177 Solna, SwedenDepartment of Health Outcomes Research and Policy, Harrison College of Pharmacy, Auburn University, Auburn, AL 36849, USADepartment of Crop Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USAMayo Clinic Artificial Intelligence & Discovery, Rochester, MN 55905, USADepartment of Industrial Design and Production Engineering, University of Aegean, 81100 Mitilini, GreeceMLV Research Group, Department of Informatics, Democritus University of Thrace, 65404 Kavala, GreeceThis study introduces Logarithmic Positional Partition Interval Encoding (LPPIE), a novel lossless compression methodology employing iterative logarithmic transformations to drastically reduce data size. While conventional dictionary-based algorithms rely on repeated sequences, LPPIE translates numeric data sequences into highly compact logarithmic representations. This achieves significant reduction in data size, especially on large integer datasets. Experimental comparisons with established compression methods—such as ZIP, Brotli, and Zstandard—demonstrate LPPIE’s exceptional effectiveness, attaining compression ratios nearly 13 times superior to established methods. However, these substantial storage savings come with elevated computational overhead due to LPPIE’s complex numerical operations. The method’s robustness across diverse datasets and minimal scalability limitations underscore its potential for specialized archival scenarios where data fidelity is paramount and processing latency is tolerable. Future enhancements, such as GPU-accelerated computations and hybrid entropy encoding integration, are proposed to further optimize performance and broaden LPPIE’s applicability. Overall, LPPIE offers a compelling alternative in lossless data compression, substantially redefining efficiency boundaries in high-volume numeric data storage.https://www.mdpi.com/2227-7080/13/7/278data compressionencoderlossless compression |
| spellingShingle | Vasileios Alevizos Zongliang Yue Sabrina Edralin Clark Xu Nikitas Gerolimos George A. Papakostas A Logarithmic Compression Method for Magnitude-Rich Data: The LPPIE Approach Technologies data compression encoder lossless compression |
| title | A Logarithmic Compression Method for Magnitude-Rich Data: The LPPIE Approach |
| title_full | A Logarithmic Compression Method for Magnitude-Rich Data: The LPPIE Approach |
| title_fullStr | A Logarithmic Compression Method for Magnitude-Rich Data: The LPPIE Approach |
| title_full_unstemmed | A Logarithmic Compression Method for Magnitude-Rich Data: The LPPIE Approach |
| title_short | A Logarithmic Compression Method for Magnitude-Rich Data: The LPPIE Approach |
| title_sort | logarithmic compression method for magnitude rich data the lppie approach |
| topic | data compression encoder lossless compression |
| url | https://www.mdpi.com/2227-7080/13/7/278 |
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