Parallel algorithm for reduction of data processing time in big data
Technological advances have allowed to collect and store large volumes of data over the years. Besides, it is significant that today's applications have high performance and can analyze these large datasets effectively. Today, it remains a challenge for data mining to make its algorithms and ap...
- Autores:
-
Silva, Jesús
H, H
Niebles Núñez, William
Ovallos-Gazabon, David
Varela, Noel
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/6240
- Acceso en línea:
- https://hdl.handle.net/11323/6240
https://repositorio.cuc.edu.co/
- Palabra clave:
- Parallel algorithm
Processing time
Big data
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Parallel algorithm for reduction of data processing time in big data |
| title |
Parallel algorithm for reduction of data processing time in big data |
| spellingShingle |
Parallel algorithm for reduction of data processing time in big data Parallel algorithm Processing time Big data |
| title_short |
Parallel algorithm for reduction of data processing time in big data |
| title_full |
Parallel algorithm for reduction of data processing time in big data |
| title_fullStr |
Parallel algorithm for reduction of data processing time in big data |
| title_full_unstemmed |
Parallel algorithm for reduction of data processing time in big data |
| title_sort |
Parallel algorithm for reduction of data processing time in big data |
| dc.creator.fl_str_mv |
Silva, Jesús H, H Niebles Núñez, William Ovallos-Gazabon, David Varela, Noel |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús H, H Niebles Núñez, William Ovallos-Gazabon, David Varela, Noel |
| dc.subject.spa.fl_str_mv |
Parallel algorithm Processing time Big data |
| topic |
Parallel algorithm Processing time Big data |
| description |
Technological advances have allowed to collect and store large volumes of data over the years. Besides, it is significant that today's applications have high performance and can analyze these large datasets effectively. Today, it remains a challenge for data mining to make its algorithms and applications equally efficient in the need of increasing data size and dimensionality [1]. To achieve this goal, many applications rely on parallelism, because it is an area that allows the reduction of cost depending on the execution time of the algorithms because it takes advantage of the characteristics of current computer architectures to run several processes concurrently [2]. This paper proposes a parallel version of the FuzzyPred algorithm based on the amount of data that can be processed within each of the processing threads, synchronously and independently. |
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2020 |
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2020-04-23T16:34:52Z |
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2020 |
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Artículo de revista |
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doi:10.1088/1742-6596/1432/1/012095 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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1742-6588 1742-6596 doi:10.1088/1742-6596/1432/1/012095 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
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[1] Chapman B, G. Jost and R Van der Pas. Using OpenMP: Portable Shared Memory Parallel Programming Scientific and Engineering Computation. The MIT Press.Massachusetts Institutte of Technology. ISBN 978-0- 262-53302-7. pp 349. 2008. [2] Jain, Mugdha, and Chakradhar Verma. "Adapting k-means for Clustering in Big Data." International Journal of Computer Applications 101.1 (2014): 19-24. [3] Ceruto T, O. Lapeira, A. Rosete and R. ESPÍN.Discovery of fuzzy predicates in database. Advances in Intelligent Systems Research (AISR Journal), vol. 51, No 1, pp. 45-54, ISSN 19516851, Atlantis Press, 2013. [4] Hariri S, and M. Parashar.Tools and Enviroments for Parallel and Distributed Computing. John Wiley & Sons. ISBN 0-471-33288-7, pag 229, 2014. [5] Fernandez A, S. Del Rio, V. Lopez, M. J. Del Jesus and F. Herrera. Big Data with Colud Computing:an insight on the computing enviroment, Map Reduce and programming frameworks. WIREs Data Mining and Knowledge Discovery.John Wiley and Sons, vol 4, pp 380-409, 2014. [6] Viloria, A. "Commercial strategies providers pharmaceutical chains for logistics cost reduction." Indian Journal of Science and Technology 8, no. 1 (2016). [7] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company. Indian Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371. [8] Pas, R. An Overview of OpenMP 3.0. In., 2009.IWOMP. Tu Dresden (Alemania). Disponible en http://iwomp.zih.tu-dresden.de/downloads/2.Overwiew_OpenMP.pdf. [9] N. Sapankevych y R. Sankar, “Time Series Prediction Using Support Vector Machines: A Survey”, IEEE Computational Intelligence Magazine, vol. 4, núm. 2, pp. 24–38, may 2009. [10] Reinders, J. Intel threading building blocks-outfitting C++ for multi-core processor parallelism. OReilly Media. ISBN 978-1449390860, pp 336, 2007. [11] Kaminsky, A. The Parallel Java 2 Library Parallel Programming in 100 % Java. Rochester Institute of Technology, Department of Computer Science, Rochester, New York, EUA. 2015. [12] F. Villada, N. Muñoz, y E. García, Aplicación de las Redes Neuronales al Pronóstico de Precios en Mercado de Valores, Información tecnológica, vol. 23, núm. 4, pp. 11–20. 2012. [13] Venugopal K, K.G. Srinivasa and L. M. Patnaik. Soft Computing for Data Mining Applications. Springer Berlin Heidelberg: Springer-Verlag. ISBN 978-3-642-00192-5, pp 354, 2009. [14] Brdar S., Culibrk D., Marinkovic B., Crnobarac J., Crnojevic V. Support Vector Machines with Features Contribution Analysis for Agricultural Yield Prediction, Second International Workshop on Sensing Technolo- gies in Agriculture, Forestry and Environment, 43-47, 2011 [15] Choudhury, A. and Jones, J. Crop yield prediction using time series models, Journal of Economics and Economic Education Research., 15, 53-68, 2014. [16] R. Putha, L. Quadrifoglio, and E. Zechman. Comparing ant colony optimization and genetic algorithm approaches for solving traffic signal coordination under oversaturation conditions. Computer‐ Aided Civil and Infrastructure Engineering, 27(1), 14-28, 2012. [17] D. Teodorović, and M. Dell’Orco. Mitigating traffic congestion: solving the ride-matching problem by bee colony optimization. Transportation Planning and Technology, 31(2), 135-152, 2008. [18] A. L. Bazzan, and F. Klügl. A review on agent-based technology for traffic and transportation. The Knowledge Engineering Review, 29(3), 375-403, 2014. [19] Amelec, V., & Alexander, P. (2015). Improvements in the automatic distribution process of finished product for pet food category in multinational company. Advanced Science Letters, 21(5), 1419-1421. [20] Karatzoglou A., Smola A., Hornik K. and Zeileis A. kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software, 11(9), 1-20, 2004 |
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Silva, JesúsH, HNiebles Núñez, WilliamOvallos-Gazabon, DavidVarela, Noel2020-04-23T16:34:52Z2020-04-23T16:34:52Z20201742-65881742-6596https://hdl.handle.net/11323/6240doi:10.1088/1742-6596/1432/1/012095Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Technological advances have allowed to collect and store large volumes of data over the years. Besides, it is significant that today's applications have high performance and can analyze these large datasets effectively. Today, it remains a challenge for data mining to make its algorithms and applications equally efficient in the need of increasing data size and dimensionality [1]. To achieve this goal, many applications rely on parallelism, because it is an area that allows the reduction of cost depending on the execution time of the algorithms because it takes advantage of the characteristics of current computer architectures to run several processes concurrently [2]. This paper proposes a parallel version of the FuzzyPred algorithm based on the amount of data that can be processed within each of the processing threads, synchronously and independently.Silva, JesúsHernandez Palma, Hugo Gaspar-will be generated-orcid-0000-0002-3873-0530-600Niebles Núñez, WilliamOvallos-Gazabon, DavidVarela, NoelengJournal of Physics: Conference SeriesRetractedCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Parallel algorithmProcessing timeBig dataParallel algorithm for reduction of data processing time in big dataArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Chapman B, G. Jost and R Van der Pas. Using OpenMP: Portable Shared Memory Parallel Programming Scientific and Engineering Computation. The MIT Press.Massachusetts Institutte of Technology. ISBN 978-0- 262-53302-7. pp 349. 2008.[2] Jain, Mugdha, and Chakradhar Verma. "Adapting k-means for Clustering in Big Data." International Journal of Computer Applications 101.1 (2014): 19-24.[3] Ceruto T, O. Lapeira, A. Rosete and R. ESPÍN.Discovery of fuzzy predicates in database. Advances in Intelligent Systems Research (AISR Journal), vol. 51, No 1, pp. 45-54, ISSN 19516851, Atlantis Press, 2013.[4] Hariri S, and M. Parashar.Tools and Enviroments for Parallel and Distributed Computing. John Wiley & Sons. ISBN 0-471-33288-7, pag 229, 2014.[5] Fernandez A, S. Del Rio, V. Lopez, M. J. Del Jesus and F. Herrera. Big Data with Colud Computing:an insight on the computing enviroment, Map Reduce and programming frameworks. WIREs Data Mining and Knowledge Discovery.John Wiley and Sons, vol 4, pp 380-409, 2014.[6] Viloria, A. "Commercial strategies providers pharmaceutical chains for logistics cost reduction." Indian Journal of Science and Technology 8, no. 1 (2016).[7] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company. Indian Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371.[8] Pas, R. An Overview of OpenMP 3.0. In., 2009.IWOMP. Tu Dresden (Alemania). Disponible en http://iwomp.zih.tu-dresden.de/downloads/2.Overwiew_OpenMP.pdf.[9] N. Sapankevych y R. Sankar, “Time Series Prediction Using Support Vector Machines: A Survey”, IEEE Computational Intelligence Magazine, vol. 4, núm. 2, pp. 24–38, may 2009.[10] Reinders, J. Intel threading building blocks-outfitting C++ for multi-core processor parallelism. OReilly Media. ISBN 978-1449390860, pp 336, 2007.[11] Kaminsky, A. The Parallel Java 2 Library Parallel Programming in 100 % Java. Rochester Institute of Technology, Department of Computer Science, Rochester, New York, EUA. 2015.[12] F. Villada, N. Muñoz, y E. García, Aplicación de las Redes Neuronales al Pronóstico de Precios en Mercado de Valores, Información tecnológica, vol. 23, núm. 4, pp. 11–20. 2012.[13] Venugopal K, K.G. Srinivasa and L. M. Patnaik. Soft Computing for Data Mining Applications. Springer Berlin Heidelberg: Springer-Verlag. ISBN 978-3-642-00192-5, pp 354, 2009.[14] Brdar S., Culibrk D., Marinkovic B., Crnobarac J., Crnojevic V. Support Vector Machines with Features Contribution Analysis for Agricultural Yield Prediction, Second International Workshop on Sensing Technolo- gies in Agriculture, Forestry and Environment, 43-47, 2011[15] Choudhury, A. and Jones, J. Crop yield prediction using time series models, Journal of Economics and Economic Education Research., 15, 53-68, 2014.[16] R. Putha, L. Quadrifoglio, and E. Zechman. Comparing ant colony optimization and genetic algorithm approaches for solving traffic signal coordination under oversaturation conditions. Computer‐ Aided Civil and Infrastructure Engineering, 27(1), 14-28, 2012.[17] D. Teodorović, and M. Dell’Orco. Mitigating traffic congestion: solving the ride-matching problem by bee colony optimization. Transportation Planning and Technology, 31(2), 135-152, 2008.[18] A. L. Bazzan, and F. Klügl. A review on agent-based technology for traffic and transportation. The Knowledge Engineering Review, 29(3), 375-403, 2014.[19] Amelec, V., & Alexander, P. (2015). Improvements in the automatic distribution process of finished product for pet food category in multinational company. Advanced Science Letters, 21(5), 1419-1421.[20] Karatzoglou A., Smola A., Hornik K. and Zeileis A. kernlab - An S4 Package for Kernel Methods in R. 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