New Efficient Approach to Solve Big Data Systems Using Parallel Gauss–Seidel Algorithms
In order to perform big-data analytics, regression involving large matrices is often necessary. In particular, large scale regression problems are encountered when one wishes to extract semantic patterns for knowledge discovery and data mining. When a large matrix can be processed in its factorized...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/6/2/43 |
| _version_ | 1797490035139805184 |
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| author | Shih Yu Chang Hsiao-Chun Wu Yifan Wang |
| author_facet | Shih Yu Chang Hsiao-Chun Wu Yifan Wang |
| author_sort | Shih Yu Chang |
| collection | DOAJ |
| description | In order to perform big-data analytics, regression involving large matrices is often necessary. In particular, large scale regression problems are encountered when one wishes to extract semantic patterns for knowledge discovery and data mining. When a large matrix can be processed in its factorized form, advantages arise in terms of computation, implementation, and data-compression. In this work, we propose two new parallel iterative algorithms as extensions of the Gauss–Seidel algorithm (GSA) to solve regression problems involving many variables. The convergence study in terms of error-bounds of the proposed iterative algorithms is also performed, and the required computation resources, namely time- and memory-complexities, are evaluated to benchmark the efficiency of the proposed new algorithms. Finally, the numerical results from both Monte Carlo simulations and real-world datasets are presented to demonstrate the striking effectiveness of our proposed new methods. |
| first_indexed | 2024-03-10T00:25:13Z |
| format | Article |
| id | doaj.art-ed4b4dabcf884e2895a68aa37320eb76 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-10T00:25:13Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-ed4b4dabcf884e2895a68aa37320eb762023-11-23T15:36:10ZengMDPI AGBig Data and Cognitive Computing2504-22892022-04-01624310.3390/bdcc6020043New Efficient Approach to Solve Big Data Systems Using Parallel Gauss–Seidel AlgorithmsShih Yu Chang0Hsiao-Chun Wu1Yifan Wang2The Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USAThe School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA 70803, USAThe Department of Computer Science, California University, Santa Clara, CA 95054, USAIn order to perform big-data analytics, regression involving large matrices is often necessary. In particular, large scale regression problems are encountered when one wishes to extract semantic patterns for knowledge discovery and data mining. When a large matrix can be processed in its factorized form, advantages arise in terms of computation, implementation, and data-compression. In this work, we propose two new parallel iterative algorithms as extensions of the Gauss–Seidel algorithm (GSA) to solve regression problems involving many variables. The convergence study in terms of error-bounds of the proposed iterative algorithms is also performed, and the required computation resources, namely time- and memory-complexities, are evaluated to benchmark the efficiency of the proposed new algorithms. Finally, the numerical results from both Monte Carlo simulations and real-world datasets are presented to demonstrate the striking effectiveness of our proposed new methods.https://www.mdpi.com/2504-2289/6/2/43Gauss–Seidel algorithmrandom iterationsmatrix factorizationlinear systemsbig data |
| spellingShingle | Shih Yu Chang Hsiao-Chun Wu Yifan Wang New Efficient Approach to Solve Big Data Systems Using Parallel Gauss–Seidel Algorithms Big Data and Cognitive Computing Gauss–Seidel algorithm random iterations matrix factorization linear systems big data |
| title | New Efficient Approach to Solve Big Data Systems Using Parallel Gauss–Seidel Algorithms |
| title_full | New Efficient Approach to Solve Big Data Systems Using Parallel Gauss–Seidel Algorithms |
| title_fullStr | New Efficient Approach to Solve Big Data Systems Using Parallel Gauss–Seidel Algorithms |
| title_full_unstemmed | New Efficient Approach to Solve Big Data Systems Using Parallel Gauss–Seidel Algorithms |
| title_short | New Efficient Approach to Solve Big Data Systems Using Parallel Gauss–Seidel Algorithms |
| title_sort | new efficient approach to solve big data systems using parallel gauss seidel algorithms |
| topic | Gauss–Seidel algorithm random iterations matrix factorization linear systems big data |
| url | https://www.mdpi.com/2504-2289/6/2/43 |
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