Show simple item record

Big Data: una exploración de investigaciones, tecnologías y casos de aplicación

dc.creatorHernández-Leal, Emilcy J.
dc.creatorDuque-Méndez, Néstor D.
dc.creatorMoreno-Cadavid, Julián
dc.date2017-05-02
dc.date.accessioned2021-10-19T20:40:25Z
dc.date.available2021-10-19T20:40:25Z
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/685
dc.identifier10.22430/22565337.685
dc.identifier.urihttp://hdl.handle.net/20.500.12622/5335
dc.descriptionBig Data has become a worldwide trend and although still lacks a scientific or academic consensual concept, every day it portends greater market growth that surrounds and the associated research areas. This paper reports a systematic review of the literature on Big Data considering a state of the art about techniques and technologies associated with Big Data, which include capture, processing, analysis and data visualization. The characteristics, strengths, weaknesses and opportunities for some applications and Big Data models that include support mainly for modeling, analysis, and data mining are explored. Likewise, some of the future trends for the development of Big Data are introduced by basic aspects, scope, and importance of each one. The methodology used for exploration involves the application of two strategies, the first corresponds to a scientometric analysis and the second corresponds to a categorization of documents through a web tool to support the process of literature review. As results, a summary and conclusions about the subject are generated and possible scenarios arise for research work in the field.en-US
dc.descriptionBig Data se ha convertido en una tendencia a nivel mundial y aunque aún no cuenta con un concepto científico o académico consensuado, se augura cada día mayor crecimiento del mercado que lo envuelve y de las áreas de investigación asociadas. En este artículo se reporta una exploración de literatura sobre Big Data, que comprende un estado del arte de las técnicas y tecnologías asociadas a Big Data, las cuales abarcan captura, procesamiento, análisis y visualización de datos. Se exploran también las características, fortalezas, debilidades y oportunidades de algunas aplicaciones y modelos que incluyen Big Data, principalmente para el soporte al modelado de datos, análisis y minería de datos. Asimismo, se introducen algunas de las tendencias futuras para el desarrollo de Big Data por medio de la definición de aspectos básicos, alcance e importancia de cada una. La metodología empleada para la exploración incluye la aplicación de dos estrategias, una primera corresponde a un análisis cienciométrico; y la segunda, una categorización de documentos por medio de una herramienta web de apoyo a los procesos de revisión literaria. Como resultados se obtiene una síntesis y conclusiones en torno a la temática y se plantean posibles escenarios para trabajos investigativos en el campo de dominio.es-ES
dc.formatapplication/pdf
dc.languagespa
dc.publisherInstituto Tecnológico Metropolitano (ITM)en-US
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/685/671
dc.relation/*ref*/K.C. Li, H. Jiang, L. T. Yang, and A. Cuzzocrea, Big Data: Algorithms, Analytics, and Applications, Chapman &. CRC Press, 2015. [2] H. Mohanty, P. Bhuyan, and D. Chenthati, Big Data: A Primer, vol. 11. Springer, 2015. [3] W. M. P. van der Aalst, “Data Scientist: The Engineer of the Future,” in Enterprise Interoperability VI, no. 7, K. Mertins, F. Bénaben, R. Poler, and J.-P. Bourrières, Eds. Springer International Publishing, 2014, pp. 13–26. [4] M. Chen, S. Mao, and Y. Liu, “Big Data: A Survey,” Mob. Networks Appl., vol. 19, no. 2, pp. 171–209, Apr. 2014. [5] L. A. Montenegro Mora, “¿Cómo elaborar un artículo de revisión?,” San Juan de Pasto, Nariño, Colombia, 2013. [6] Elsevier, “Scopus The largest database of peer-reviewed literature,” Scopus Elsevier. 2016. [Online]. Available: https://www.elsevier.com/solutions/scopus. [7] S. Robledo Giraldo, G. Osorio Zuluaga, and C. López Espinosa, “Networking en pequeña empresa: una revisión bibliográfica utilizando la teoría de grafos,” Rev. Vínculos, vol. 11, no. 2, pp. 6–16, 2014. [8] J. Dean and S. Ghemawat, “MapReduce,” Commun. ACM, vol. 51, no. 1, p. 107, Jan. 2008. [9] M. Armbrust, I. Stoica, M. Zaharia, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, and A. Rabkin, “A view of cloud computing,” Commun. ACM, vol. 53, no. 4, p. 50, Apr. 2010. [10] P. Zikopoulos and C. Eaton, Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, 1st ed. McGraw-Hill Osborne Media, 2011. [11] T. White, Hadoop: The Definitive Guide, 2nd ed. United States of America: O’Reilly Media, Inc, 2010. [12] D. Bollier, “The Promise and Peril of Big Data,” Washington, DC, 2010. [13] C. L. P. Chen and C.-Y. Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on Big Data,” Inf. Sci. (Ny)., vol. 275, pp. 314–347, 2014. [14] J. Chen, Y. Chen, X. Du, C. Li, J. Lu, S. Zhao, and X. Zhou, “Big data challenge: a data management perspective,” Front. Comput. Sci., vol. 7, no. 2, pp. 157–164, Apr. 2013. [15] X. Jin, B. W. Wah, X. Cheng, and Y. Wang, “Significance and Challenges of Big Data Research,” Big Data Res., vol. 2, no. 2, pp. 59–64, Jun. 2015. [16] Xindong Wu, Xingquan Zhu, Gong-Qing Wu, and Wei Ding, “Data mining with big data,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 1, pp. 97–107, Jan. 2014. [17] T. A. S. Foundation, “Welcome to ApacheTM Hadoop®!,” hadoop. 2016. [Online]. Availa-ble: http://hadoop.apache.org/ [18] M. Klein, R. Sharma, C. H. Bohrer, C. M. Avelis, and E. Roberts, “Biospark: scalable analysis of large numerical datasets from biological simulations and experiments using Hadoop and Spark,” Bioinformatics, vol. 33, no. 2, pp. 303–305, Jan. 2017. [19] A. Aji, F. Wang, H. Vo, R. Lee, Q. Liu, X. Zhang, and J. Saltz, “Hadoop GIS: a high performance spatial data warehousing system over mapreduce,” Proc. VLDB Endow., vol. 6, no. 11, pp. 1009–1020, 2013. [20] A. M. Aly, H. Elmeleegy, Y. Qi, and W. Aref, “Kangaroo,” in Proceedings of the Ninth ACM International Conference on Web Search and Data Mining - WSDM ’16, 2016, pp. 397–406. [21] R. Lämmel, “Google’s MapReduce programming model — Revisited,” Sci. Comput. Program., vol. 70, no. 1, pp. 1–30, Jan. 2008. [22] K. Lee, K. Jung, J. Park, and D. Kwon, “ARLS: A MapReduce-based output analysis tool for large-scale simulations,” Adv. Eng. Softw., vol. 95, pp. 28–37, May 2016. [23] J.-D. Wang, “Extracting significant pattern histories from timestamped texts using MapReduce,” J. Supercomput., vol. 72, no. 8, pp. 3236–3260, Aug. 2016. [24] H. Zhang and N. Xiao, “Parallel implementation of multilayered neural networks based on Map-Reduce on cloud computing clusters,” Soft Comput., vol. 20, no. 4, pp. 1471–1483, Apr. 2016. [25] Y. Ji, Y. Tian, F. Shen, and J. Tran, “Experimental Evaluations of MapReduce in Biomedical Text Mining,” in Information Technology: New Generations, Springer, 2016, pp. 665–675. [26] S. Singh and N. Ahuja, “Article recommendation system based on keyword using map-reduce,” in 2015 Third International Conference on Image Information Processing (ICIIP), 2015, pp. 548–550. [27] T. A. S. Foundation, “Apache HBase,” Apache HBase. 2016. [Online]. Available: http://hbase.apache.org/ [28] G. C. Deka, “A Survey of Cloud Database Systems,” IT Prof., vol. 16, no. 2, pp. 50–57, Mar. 2014. [29] T. A. S. Foundation, “The Apache Cassandra Project,” Apache Cassandra. 2015. [30] E. Dede, B. Sendir, P. Kuzlu, J. Hartog, and M. Govindaraju, “An Evaluation of Cassandra for Hadoop,” in 2013 IEEE Sixth International Conference on Cloud Computing, 2013, vol. 2013, pp. 494–501. [31] T. A. S. Foundation, “Apache Mahout: Scalable machine learning and data mining,” Apache Mahout. 2016. [32] G. Ingersoll, “Introducing Apache Mahout,” IBM developerWorks. 2009. [Online]. Availa-ble: http://www.ibm.com/developerworks/java/library/j-mahout/ [33] G. Ingersoll, “Apache Mahout: Aprendizaje escalable con máquina para todos,” IBM developerWorks. 2012. [Online]. Available: http://www.ibm.com/developerworks/ssa/library/j-mahout-scaling/ [34] S. M. D. MUJEEB and L. K. NAIDU, “A Relative Study on Big Data Applications and Techniques,” Int. J. Eng. Innov. Technol., vol. 4, no. 10, pp. 133–138, 2015. [35] J. Han, J. Pei, and M. Kamber, “Data mining: concepts and techniques,” 3rd ed., E. Inc., Ed. Morgan Kaufmann Publishers, 2011, p. 703. [36] R. L. Grossman, C. Kamath, P. Kegelmeyer, V. Kumar, and R. Namburu, Data Mining for Scientific and Engineering Applications, vol. 2. Boston, MA: Springer US, 2013. [37] R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, Machine learning: An artificial intelligence approach. Springer Science & Business Media, 2013.
dc.relation/*ref*/P. Domingos, “A few useful things to know about machine learning,” Commun. ACM, vol. 55, no. 10, p. 78, Oct. 2012. [39] I. Portugal, P. Alencar, and D. Cowan, “The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review,” arXiv, vol. 4, pp. 1–16, Nov. 2015. [40] M. Crawford, T. M. Khoshgoftaar, J. D. Prusa, A. N. Richter, and H. Al Najada, “Survey of review spam detection using machine learning techniques,” J. Big Data, vol. 2, no. 1, p. 23, Dec. 2015. [41] Wei-Yang Lin, Ya-Han Hu, and Chih-Fong Tsai, “Machine Learning in Financial Crisis Prediction: A Survey,” IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev., vol. 42, no. 4, pp. 421–436, Jul. 2012. [42] R. Dash and P. K. Dash, “A hybrid stock trading framework integrating technical analysis with machine learning techniques,” J. Financ. Data Sci., vol. 2, no. 1, pp. 42–57, Mar. 2016. [43] J. Patel, S. Shah, P. Thakkar, and K. Kotecha, “Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques,” Expert Syst. Appl., vol. 42, no. 1, pp. 259–268, Jan. 2015. [44] E. Cuevas, D. Zaldívar, and M. Perez-Cisneros, Applications of Evolutionary Computation in Image Processing and Pattern Recognition, 1st ed., vol. 100. Cham: Springer International Publishing, 2016. [45] K.-F. Man, K. S. TANG, and S. Kwong, Genetic Algorithms: Concepts and Designs. Springer Science & Business Media, 2012. [46] G. Luque and E. Alba, Parallel Genetic Algorithms: Theory and Real World Applications, vol. 367. Springer, 2011. [47] U. Maulik, S. Bandyopadhyay, and A. Mukhopadhyay, Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics. Springer Science & Business Media, 2011. [48] A. E. Doub, M. L. Small, A. Levin, K. LeVangie, and T. R. Brick, “Identifying users of traditional and Internet-based resources for meal ideas: An association rule learning approach,” Appetite, vol. 103, pp. 128–136, Aug. 2016. [49] H. Sundell, R. Konig, and U. Johansson, “Pragmatic Approach to Association Rule Learning in Real-World Scenarios,” in 2015 International Conference on Computational Science and Computational Intelligence (CSCI), 2015, pp. 356–361. [50] R. Sarno, R. D. Dewandono, T. Ahmad, M. F. Naufal, and F. Sinaga, “Hybrid association rule learning and process mining for fraud detection,” IAENG Int. J. Comput. Sci., vol. 42, no. 2, pp. 1–14, 2015. [51] S. Jaramillo Valbuena and J. M. Londoño, “Sistemas para almacenar grandes volúmenes de datos,” Rev. Gerenc. Tecnológica Informática, vol. 13, no. 37, pp. 17–28, 2015. [52] S. Sagiroglu and D. Sinanc, “Big data: A review,” in 2013 International Conference on Collaboration Technologies and Systems (CTS), 2013, pp. 42–47. [53] D. E. O’Leary, “Artificial Intelligence and Big Data,” IEEE Intell. Syst., vol. 28, no. 2, pp. 96–99, Mar. 2013. [54] A. Gandomi and M. Haider, “Beyond the hype: Big data concepts, methods, and analytics,” Int. J. Inf. Manage., vol. 35, no. 2, pp. 137–144, Apr. 2015.
dc.rightshttps://creativecommons.org/licenses/by/3.0/deed.es_ESen-US
dc.sourceTecnoLógicas; Vol. 20 No. 39 (2017); 15-38en-US
dc.sourceTecnoLógicas; Vol. 20 Núm. 39 (2017); 15-38es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectBig dataen-US
dc.subjectdata analysisen-US
dc.subjectdata scienceen-US
dc.subjectdata miningen-US
dc.subjectbig data analysisen-US
dc.subjectBig Dataes-ES
dc.subjectanálisis de datoses-ES
dc.subjectciencia de los datoses-ES
dc.subjectminería de datoses-ES
dc.subjectanálisis Big Dataes-ES
dc.titleBig Data: an exploration of research, technologies and application casesen-US
dc.titleBig Data: una exploración de investigaciones, tecnologías y casos de aplicaciónes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record