Big Data: desarrollo, avance y aplicación en las Organizaciones de la era de la Información

  • Juan Camilo Duque-Jaramillo Universidad Nacional de Colombia
  • Eliana María Villa-Enciso Instituto Tecnológico Metropolitano
Palabras clave: Big Data, sistemas de información, tendencias, innovación, toma de decisiones, modelo administrativo, gestión tecnológica

Resumen

Con el avance de la era de la información y el conocimiento, se han diseñado nuevas técnicas que permiten el desarrollo, creación y evolución de estrategias, instrumentos y aplicaciones que facilitan las acciones internas y externas de las organizaciones. El propósito del presente artículo es evidenciar los resultados de una revisión documental de una de estas técnicas: el Big Data. La metodología utilizada consistió en una revisión de la base de datos Scopus, en una ventana de observación de los últimos dos años (2014-2016). Con base en lo encontrado, se discutió sobre la importancia, ventajas y desventajas de esta aplicación. Se evidenció que el uso del Big Data es una técnica importante que contribuye al manejo de grandes volúmenes de información y que es usada en el campo de la gestión tecnológica en las organizaciones; se demostró la existencia de organizaciones que a través de la implementación y uso del Big Data han logrado aumentar su ventaja competitiva con respecto a las demás, ya que les ha permitido reducir costos en operaciones, facilitar procesos, diseñar nuevos modelos de negocio y tomar decisiones organizacionales que se han vuelto más difíciles de tomar, ya que cada día es más grande el volumen de información que se maneja

Biografía del autor/a

Juan Camilo Duque-Jaramillo, Universidad Nacional de Colombia
Estudiante de Ingeniería industrial, Universidad Nacional de Colombia
Eliana María Villa-Enciso, Instituto Tecnológico Metropolitano
MSc. en Ingeniería Administrativa, Instituto Tecnológico Metropolitano

Referencias bibliográficas

Adhikari, A.; Hojjati, A.; Shen, J.; Hsu, J.-T.; King, W. P. y Winslett, M. (2016). Trust Issues for Big Data about High-Value Manufactured Parts. En 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS) (pp. 24–29). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7502259

Agahi, F. y Dmytrenko, N. (2016). Chunking decision information: a way to make big data actionable. Journal of Decision Systems, 25(sup1), 11–22.

Ahmad, A.; Ahmad, R. y Hashim, K. F. (2016). Innovation Traits for Business Inteligence Succesful inteligence Deployment. Journal of Theoretical and Applied Information Technology, 89(1). Recuperado de http://www.jatit.org/volumes/Vol89No1/11Vol89No1.pdf

At, L. (2016). Xiaoyi Lu at OSU. Recuperado de http://web.cse.ohio-state.edu/~luxi/

Bacca, G. (2013). Blog de Innovación. Recuperado de http://www.educacionline.com/instituto-de-marketing-online/big-data-y-marketing-digital-una-relacion-imprescindible/

Baillie, J. (2016). How’Big data’will drive future innovation. Health estate, 70(3), 59–64.

Banerjee, S. (2016). Influence of consumer personality, brand personality, and corporate personality on brand preference: An empirical investigation of interaction effect. Asia Pacific Journal of Marketing and Logistics, 28(2), 198–216.

Barranco, R. (2012). ¿Qué es Big Data? [CT316]. Recuperado de https://www.ibm.com/developerworks/ssa/local/im/que-es-big-data/

Bhukya, R. y Gyani, J. (2015). Fuzzy associative classification algorithm based on MapReduce framework. En 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) (pp. 357–360). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7456909

Bondarev, A. y Zakirov, D. (2015). Data warehouse on Hadoop platform for decision support systems in education. En 2015 Twelve International Conference on Electronics Computer and Computation (ICECCO) (pp. 1–4). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7416884

Brown, L. (2016). Vertical and horizontal approaches to the making of racial statistics in Britain. Ethnic and Racial Studies, 1–19.

Calvard, T. S. (2015a). Big data, organizational learning, and sensemaking: Theorizing interpretive challenges under conditions of dynamic complexity. Management learning, 1350507615592113.

Calvard, T. S. (2015b). Big data, organizational learning, and sensemaking: Theorizing interpretive challenges under conditions of dynamic complexity. Management learning, 1350507615592113.

Campion, M. C.; Campion, M. A.; Campion, E. D. y Reider, M. H. (2016). Initial Investigation Into Computer Scoring of Candidate Essays for Personnel Selection. Recuperado de http://psycnet.apa.org/psycinfo/2016-18130-001/

Cawley, A. (2016). Is There a Press Release on That? The Challenges and Opportunities of Big Data for News Media. En Big Data Challenges (pp. 49–58). Springer. Recuperado de http://link.springer.com/chapter/10.1057/978-1-349-94885-7_5

Chang, V.; Ramachandran, M.; Wills, G.; Walters, R. J., Li, C.-S. y Watters, P. (2016). Editorial for FGCS special issue: Big Data in the cloud. Future Generation Computer Systems. Recuperado de http://www.sciencedirect.com/science/article/pii/S0167739X1630084X

Chianese, A.; Marulli, F. y Piccialli, F. (2016). Cultural Heritage and Social Pulse: A Semantic Approach for CH Sensitivity Discovery in Social Media Data. En 2016 IEEE Tenth International Conference on Semantic Computing (ICSC) (pp. 459–464). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7439386

Costantini, E. A.; Lorenzetti, R. y Malorgio, G. (2016). A multivariate approach for the study of environmental drivers of wine economic structure. Land Use Policy, 57, 53–63.

Cowls, J. y Schroeder, R. (2015a). Causation, correlation, and big data in social science research. Policy & Internet, 7(4), 447–472.

Cowls, J. y Schroeder, R. (2015b). Causation, correlation, and big data in social science research. Policy & Internet, 7(4), 447–472.

Deutsch, R.; Leed, A. P. y others. (2015). Leveraging data Across the Building Lifecycle. Procedia Engineering, 118, 260–267.

Gim, J.; Lee, J.; Jang, Y.; Jeong, D.-H. y Jung, H. (2016). A Trend Analysis Method for IoT Technologies Using Patent Dataset with Goal and Approach Concepts. Wireless Personal Communications, 1–16.

Hill, C. y Jones, G. (1996). Hill, C. y Jones, G. (1996). Administración estratégica. Estados Unidos: Mc Graw Hill. Recuperado de https://www.google.com.co/gfe_rd=cr&ei=Lc_IV7WzCIaDmAGjv4H4Bw&gws_rd=ssl#q=Hill%2C+C.%2C+%26+Jones%2C+G.+(1996).+Administraci%C3%B3n+estrat%C3%A9gica.+Estados+Unidos:+Mc+Graw+Hill

IBM (2016). IBM Data Magazine: Its Value and Its Vision. Recuperado de http://www.ibmbigdatahub.com/blog/ibm-data-magazine-its-value-and-its-vision

Islam, N. (2015). Nusrat Islam. Recuperado de http://web.cse.ohio-state.edu/~islamn/

Islam, N. S.; Lu, X.; Wasi-ur-Rahman, M.; Jose, J. y Panda, D. K. D. (2014). A micro-benchmark suite for evaluating HDFS operations on modern clusters. En Specifying Big Data Benchmarks (pp. 129–147). Springer. Recuperado de http://link.springer.com/chapter/10.1007/978-3-642-53974-9_12

Islam, N. S.; Lu, X.; Wasi-ur-Rahman, M.; Rajachandrasekar, R. y Panda, D. K. D. (2014). In-memory i/o and replication for hdfs with memcached: Early experiences. En Big Data (Big Data), 2014 IEEE International Conference on (pp. 213–218). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7004235

Islam, N. S.; Lu, X.; Wasi-ur-Rahman, M.; Shankar, D. y Panda, D. K. (2015). Triple-H: A hybrid approach to accelerate HDFS on HPC clusters with heterogeneous storage architecture. En Cluster, Cloud and Grid Computing (CCGrid), 2015 15th IEEE/ACM International Symposium on (pp. 101–110). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7152476

Islam, N. S.; Shankar, D.; Lu, X.; Wasi-Ur-Rahman, M. y Panda, D. K. (2015). Accelerating I/O Performance of Big Data Analytics on HPC Clusters through RDMA-Based Key-Value Store. En Parallel Processing (ICPP), 2015 44th International Conference on (pp. 280–289). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7349583

Islam, N. S.; Wasi-ur-Rahman, M.; Lu, X.; Shankar, D. y Panda, D. K. (2015). Performance characterization and acceleration of in-memory file systems for Hadoop and Spark applications on HPC clusters. En Big Data (Big Data), 2015 IEEE International Conference on (pp. 243–252). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7363761

Jang, K.; Lee, K.; Jang, G.; Jung, S.; Seo, M. G. y Myaeng, S.H. (2016). Food hazard event extraction based on news and social media: A preliminary work. En 2016 International Conference on Big Data and Smart Computing (BigComp) (pp. 466–469). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7425972

Jianqiang, Z. y Xueliang, C. (2015). Combining Semantic and Prior Polarity for Boosting Twitter Sentiment Analysis. En 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity) (pp. 832–837). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7463826

Kaivo-oja, J.; Virtanen, P. Jalonen, H. y Stenvall, J. (2015a). The effects of the internet of Things and big data to organizations and their knowledge management practices. En International Conference on Knowledge Management in Organizations (pp. 495–513). Springer. Recuperado de http://link.springer.com/chapter/10.1007/978-3-319-21009-4_38

Kaivo-oja, J.; Virtanen, P.; Jalonen, H. Stenvall, J. (2015b). The effects of the internet of Things and big data to organizations and their knowledge management practices. En International Conference on Knowledge Management in Organizations (pp. 495–513). Springer. Recuperado de http://link.springer.com/chapter/10.1007/978-3-319-21009-4_38

Kallinikos, J. y Constantiou, I. D. (2015). Big data revisited: a rejoinder. Journal of Information Technology, 30(1), 70–74.

Khade, A. A. (2016). Performing Customer Behavior Analysis using Big Data Analytics. Procedia Computer Science, 79, 986–992.

Klievink, B.; Romijn, B.-J.; Cunningham, S. y de Bruijn, H. (2016). Big data in the public sector: Uncertainties and readiness. Information Systems Frontiers, 1–17.

Larson, D. y Chang, V. (2016). A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), 700–710.

Lewis, J.; Liaw, S.-T. y Ray, P. (2015). Applying «big data» and business intelligence insights to improving clinical care for cancer. En 2015 IEEE International Symposium on Technology and Society (ISTAS) (pp. 1–4). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7439399

Lomotey, R. K. y Deters, R. (2013a). Real-Time Effective Framework for Unstructured Data Mining. En 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (pp. 1081–1088). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6680952

Lomotey, R. K. y Deters, R. (2013b). RSenter: tool for topics and terms extraction from unstructured data debris. En 2013 IEEE International Congress on Big Data (pp. 395–402). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6597163

Lomotey, R. K. y Deters, R. (2013c). Topics and terms mining in unstructured data stores. En Computational Science and Engineering (CSE), 2013 IEEE 16th International Conference on (pp. 854–861). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6755309

Lomotey, R. K. y Deters, R. (2014a). Analytics-as-a-service (aaas) tool for unstructured data mining. En Cloud Engineering (IC2E), 2014 IEEE International Conference on (pp. 319–324). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6903489

Lomotey, R. K. y Deters, R. (2014b). Towards knowledge discovery in big data. En Service Oriented System Engineering (SOSE), 2014 IEEE 8th International Symposium on (pp. 181–191). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6830904

López, D. (2012). Análisis de las posibilidades de uso de Big Data en las organizaciones. Santander, España. Recuperado de https://www.google.com.co/gfe_rd=cr&ei=Lc_IV7WzCIaDmAGjv4H4Bw&gws_rd=ssl#q=L%C3%B3pez%2C+D.+(2012).+An%C3%A1lisis+de+las+posibilidades+de+uso+de+Big+Data+en+las+Organizaciones.+Santander%2C+Espa%C3%B1a.

López, V.; Mi, G.; Gonz, B.; Valverde, G.; Caro, R. and others. (2015). Big+ Open Data: Some applications for a Smartcity. En 2015 IEEE International Conference on Progress in Informatics and Computing (PIC) (pp. 384–389). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7489874

Lu, Y. y Sinnott, R. O. (s. f.). Semantic Security for e-Health: A Case Study in Enhanced Access Control. challenge, 6, 7.

Marfo, J. S. y Boateng, R. (2015a). Big Data and Organizational Learning: Conceptualizing the Link. En International Conference on E-Learning, E-Education, and Online Training (pp. 159–164). Springer. Recuperado de http://link.springer.com/chapter/10.1007/978-3-319-28883-3_20

Marfo, J. S. y Boateng, R. (2015b). Big Data and Organizational Learning: Conceptualizing the Link. En International Conference on E-Learning, E-Education, and Online Training (pp. 159–164). Springer. Recuperado de http://link.springer.com/chapter/10.1007/978-3-319-28883-3_20

Md. Wasi-ur-Rahman (2016). Md. Wasi-ur- Rahman. Recuperado de http://web.cse.ohio-state.edu/~rahmanmd/
Mihály, F. (2016). An objective corruption risk index using public procurement data. Recuperado de https://www.repository.cam.ac.uk/handle/1810/254009

Mix News Colombia (2015). Mix News Colombia: «Big Data World Insights 2015». Recuperado de http://mixnewscolombia.blogspot.com/2015/07/big-data-world-insights-2015.html

Motta, G.; Puccinelli, R.; Reggiani, L. y Saccone, M. (2016a). Extracting Value from Grey Literature: processes and technologies for aggregating and analyzing the hidden «big data» treasure of organizations. Grey Journal (TGJ), 12(1). Recuperado de https://www.researchgate.net/profile/Massimiliano_Saccone/publication/298713220_Extracting_Value_from_Grey_Literature_processes_and_technologies_for_aggregating_and_analyzing_the_hidden_big_data_treasure_of_organizations/links/56ea70ed08aec8bc0781bc92.pdf

Motta, G.; Puccinelli, R.; Reggiani, L. y Saccone, M. (2016b). Extracting Value from Grey Literature: processes and technologies for aggregating and analyzing the hidden «big data» treasure of organizations. Grey Journal (TGJ), 12(1). Recuperado de https://www.researchgate.net/profile/Massimiliano_Saccone/publication/298713220_Extracting_Value_from_Grey_Literature_processes_and_technologies_for_aggregating_and_analyzing_the_hidden_big_data_treasure_of_organizations/links/56ea70ed08aec8bc0781bc92.pdf

MSP Comunications (2016). IBM Systems Magazine - Data Management. Recuperado de http://www.ibmsystemsmag.com/power/Systems-Management/Data-Management/

Mwilu, O. S.; Comyn-Wattiau, I. y Prat, N. (2015). Design science research contribution to business intelligence in the cloud—A systematic literature review. Future Generation Computer Systems. Recuperado de http://www.sciencedirect.com/science/article/pii/S0167739X15003623

Nambiar, R. (2014). Benchmarking big data systems: introducing TPC express benchmark HS. En Workshop on Big Data Benchmarks (pp. 24–28). Springer. Recuperado de http://link.springer.com/chapter/10.1007/978-3-319-20233-4_3

Nambiar, R.; Chitor, R. y Joshi, A. (2014). Data Management–A Look Back and a Look Ahead. En Specifying Big Data Benchmarks (pp. 11–19). Springer. Recuperado de http://link.springer.com/chapter/10.1007/978-3-642-53974-9_2

Nambiar, R. y Poess, M. (2015). Reinventing the TPC: From Traditional to Big Data to Internet of Things. En R. Nambiar & M. Poess (Eds.). Performance Evaluation and Benchmarking: Traditional to Big Data to Internet of Things (pp. 1-7). Springer International Publishing. https://doi.org/10.1007/978-3-319-31409-9_1

Nambiar, R.; Poess, M.; Masland, A.; Taheri, H. R.; Bond, A.; Carman, F. y Majdalany, M. (2013). TPC State of the Council 2013. En Technology Conference on Performance Evaluation and Benchmarking (pp. 1–15). Springer. Recuperado de http://link.springer.com/chapter/10.1007/978-3-319-04936-6_1

Netten, N.; van den Braak, S.; Choenni, S. y van Someren, M. (2016). A Big Data Approach to Support Information Distribution in Crisis Response. En Proceedings of the 9th International Conference on Theory and Practice of Electronic Governance (pp. 266–275). ACM. Recuperado de http://dl.acm.org/citation.cfm?id=2910033

Olivares, J. (2016). Big Data: la explosión del universo digital y oportunidad de negocio. Recuperado de 2016, a partir de http://www.docpath.com/es/art-big-data-document-technology-software.aspx

Oracle (s. f.). Business Intelligence (BI) | Oracle España. Recuperado de 2016, a partir de https://www.oracle.com/es/solutions/business-analytics/business-intelligence/index.html

Osuszek, L.; Stanek, S. y Twardowski, Z. (2016). Leverage big data analytics for dynamic informed decisions with advanced case management. Journal of Decision Systems, 25(sup1), 436–449.

Panda, D. (2016a). Dhabaleswar K. Panda. Recuperado de http://web.cse.ohio-state.edu/~panda/

Panda, D. (2016b). NOWLAB: Network Based Computing Lab- Home. Recuperado de http://nowlab.cse.ohio-state.edu/

Prasad, S.; Zakaria, R. y Altay, N. (2016). Big data in humanitarian supply chain networks: a resource dependence perspective. Annals of Operations Research, 1–31.

Rady, S. (2015). A business intelligent technique for sentiment estimation by management sectors. En 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) (pp. 370–376). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7397247

Ramannavar, M. y Sidnal, N. S. (2016). Big Data and Analytics—A Journey Through Basic Concepts to Research Issues. En Proceedings of the International Conference on Soft Computing Systems (pp. 291–306). Springer. Recuperado de http://link.springer.com/chapter/10.1007/978-81-322-2674-1_29

Sachdeva, N.; Singh, O.; Kapur, P. K. y Galar, D. (s. f.). Multi-criteria intuitionistic fuzzy group decision analysis with TOPSIS method for selecting appropriate cloud solution to manage big data projects. International Journal of System Assurance Engineering and Management, 1–9.

Santos, M. Y. y Costa, C. (2016). Data Warehousing in Big Data: From Multidimensional to Tabular Data Models. En Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering (pp. 51–60). ACM. Recuperado de http://dl.acm.org/citation.cfm?id=2949024

Severiens, T. (2015). Quality Measurement beyond Bibliometry. En Advanced Applied Informatics (IIAI-AAI), 2015 IIAI 4th International Congress on (pp. 483–486). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7373957

Severo, M.; Feredj, A. y Romele, A. (2016). Soft Data and Public Policy: Can Social Media Offer Alternatives to Official Statistics in Urban Policymaking? Policy & Internet. Recuperado de http://onlinelibrary.wiley.com/doi/10.1002/poi3.127/full

Singhal, R.; Nambiar, M.; Sukhwani, H. y Trivedi, K. (2014). Performability Comparison of Lustre and HDFS for MR Applications. En Software Reliability Engineering Workshops (ISSREW), 2014 IEEE International Symposium on (pp. 51–51). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6983800

Sinnott, R. O. (2016). The Australian Data-Driven Urban Research Platform: Systems Paper. Australian Economic Review, 49(2), 208–223.

Sinnott, R. O.; Bayliss, C.; Bromage, A.; Galang, G.; Gong, Y.; Greenwood, P. and others (2016). Privacy Preserving Geo-Linkage in the Big Urban Data Era. Journal of Grid Computing, 1–16.

Sinnott, R. O. y Chen, W. (2016). Estimating crowd sizes through social media. En 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) (pp. 1–6). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7457123

Sinnott, R. O.; Morandini, L. y Wu, S. (2015). SMASH: A Cloud-based Architecture for Big Data Processing and Visualization of Traffic Data. En 2015 IEEE International Conference on Data Science and Data Intensive Systems (pp. 53–60). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7396481

Soergel, D. (2015). Unleashing the Power of Data Through Organization: Structure and Connections for Meaning, Learning and Discovery. Knowledge Organization, 42(6), 401–427.

Springer. (2016). Lecture Notes in Computer Science LNCS | Springer. Recuperado de http://www.springer.com/gp/computer-science/lncs

Tan, K. H.; Ortiz-Gallardo, V. G. y Perrons, R. K. (2016). Using Big Data to manage safety-related risk in the upstream oil & gas industry: A research agenda. Energy Exploration & Exploitation, 34(2), 282–289.

TICbeat (2012). Cómo deben usar las empresas Twitter (según Twitter). Recuperado de http://www.ticbeat.com/socialmedia/como-usar-twitter-empresas/

Vashisht, P. y Gupta, V. (2015). Big data analytics techniques: A survey. En Green Computing and Internet of Things (ICGCIoT), 2015 International Conference on (pp. 264–269). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7380470

Verma, S.; Verma, S.; Sekhar Bhattacharyya, S. y Sekhar Bhattacharyya, S. (2016). Micro-foundation strategies of IOT, BDA, Cloud Computing: Do they really matter in bottom of pyramid? Strategic Direction, 32(8), 36–38.

Viaña, E. (2015). El vértigo del «big data» y algunas empresas que lo utilizan bien. Recuperado de http://www.expansion.com/directivos/2015/05/20/555cde3b1.html

Wang, G.; Gunasekaran, A.; Ngai, E. W. y Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98–110.

Wasi-ur-Rahman, M.; Lu, X.; Islam, N. S.; Rajachandrasekar, R. y Panda, D. K. (2015). High-performance design of YARN MapReduce on modern HPC clusters with Lustre and RDMA. En Parallel and Distributed Processing Symposium (IPDPS), 2015 IEEE International (pp. 291–300). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7161518

Wise, C.; Friedrich, C.; Nepal, S.; Chen, S. y Sinnott, R. O. (2015). Cloud Docs: Secure Scalable Document Sharing on Public Clouds. En 2015 IEEE 8th International Conference on Cloud Computing (pp. 532–539). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7214087

Wu, W.; Lu, X.; Cox, B.; Li, G.; Lin, L.; Yang, Q. y others. (2014). Retrieving Information and Discovering Knowledge from Unstructured Data Using Big Data Mining Technique: Heavy Oil Fields Example. En International Petroleum Technology Conference. International Petroleum Technology Conference. Recuperado de https://www.onepetro.org/conference-paper/IPTC-17805-MS

Ylijoki, O. y Porras, J. (2016a). Conceptualizing Big Data: Analysis of Case Studies. Intelligent Systems in Accounting, Finance and Management. Recuperado de http://onlinelibrary.wiley.com/doi/10.1002/isaf.1393/full

Ylijoki, O. y Porras, J. (2016b). Conceptualizing Big Data: Analysis of Case Studies. Intelligent Systems in Accounting, Finance and Management. Recuperado de http://onlinelibrary.wiley.com/doi/10.1002/isaf.1393/full

Yu, X. y Wu, S. (2015). Typical Applications of Big Data in Education. En 2015 International Conference of Educational Innovation through Technology (EITT) (pp. 103–106). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7446158

Zeng, J. y Plale, B. (2016). KVLight: A Lightweight Key-Value Store for Distributed Access in Cloud. En 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) (pp. 473–482). IEEE. Recuperado de http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7515724

Zhu, Y.; Zhan, J.; Weng, C.; Nambiar, R.; Zhang, J.; Chen, X. y Wang, L. (2014). Bigop: Generating comprehensive big data workloads as a benchmarking framework. En International Conference on Database Systems for Advanced Applications (pp. 483–492). Springer. Recuperado de http://link.springer.com/chapter/10.1007/978-3-319-05813-9_32
Cómo citar
Duque-Jaramillo, J. C., & Villa-Enciso, E. M. (2016). Big Data: desarrollo, avance y aplicación en las Organizaciones de la era de la Información. Revista CEA, 2(4), 27–45. https://doi.org/10.22430/24223182.169

Descargas

Los datos de descargas todavía no están disponibles.
Publicado
2016-07-30
Sección
Artículos

Métricas

Algunos artículos similares: