Risk analysis of using big data in computer sciences

Today, as technologies mature and people are encouraged to contribute data to organizations’ databases, more transactions are being captured than ever before. Meanwhile, improvements in data storage technologies have made the cost of evaluating, selecting, and destroying legacy data considerably gre...

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Autores:
Silva, Jesus
Pineda Lezama, Omar Bonerge
Romero Marin, Ligia Cielo
Solano, Darwin
Silva Fernández, Claudia Susana
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/5989
Acceso en línea:
https://hdl.handle.net/11323/5989
https://repositorio.cuc.edu.co/
Palabra clave:
Data management
Data quality
Decision making
Data analysis
Gestión de datos
Calidad de datos
Toma de decisiones
Análisis de datos
Rights
openAccess
License
CC0 1.0 Universal
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dc.title.spa.fl_str_mv Risk analysis of using big data in computer sciences
dc.title.translated.spa.fl_str_mv Análisis de riesgos del uso de big data en ciencias de la computación
title Risk analysis of using big data in computer sciences
spellingShingle Risk analysis of using big data in computer sciences
Data management
Data quality
Decision making
Data analysis
Gestión de datos
Calidad de datos
Toma de decisiones
Análisis de datos
title_short Risk analysis of using big data in computer sciences
title_full Risk analysis of using big data in computer sciences
title_fullStr Risk analysis of using big data in computer sciences
title_full_unstemmed Risk analysis of using big data in computer sciences
title_sort Risk analysis of using big data in computer sciences
dc.creator.fl_str_mv Silva, Jesus
Pineda Lezama, Omar Bonerge
Romero Marin, Ligia Cielo
Solano, Darwin
Silva Fernández, Claudia Susana
dc.contributor.author.spa.fl_str_mv Silva, Jesus
Pineda Lezama, Omar Bonerge
Romero Marin, Ligia Cielo
Solano, Darwin
Silva Fernández, Claudia Susana
dc.subject.spa.fl_str_mv Data management
Data quality
Decision making
Data analysis
Gestión de datos
Calidad de datos
Toma de decisiones
Análisis de datos
topic Data management
Data quality
Decision making
Data analysis
Gestión de datos
Calidad de datos
Toma de decisiones
Análisis de datos
description Today, as technologies mature and people are encouraged to contribute data to organizations’ databases, more transactions are being captured than ever before. Meanwhile, improvements in data storage technologies have made the cost of evaluating, selecting, and destroying legacy data considerably greater than simply letting it accumulate. On the one hand, the excess of stored data has considerably increased the opportunities to interrelate and analyze them, while the moderate enthusiasm generated by data warehousing and data mining in the 1990s has been replaced by a rampant euphoria about big data and data analytics. But, is this as wonderful as seems? This paper presents a risk analysis of Big Data and Big Data Analytics based on a review of quality factors.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2020-02-05T13:28:32Z
dc.date.available.none.fl_str_mv 2020-02-05T13:28:32Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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dc.relation.references.spa.fl_str_mv [1] Schroeck, M. et al. (2012). Analytics: The real-world use of big data. IBM Institute for Business Value. University of Oxford.
[2] McAfee, A. & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Rev. 90, 61–68.
[3] Clarke, R. (2014). Promise unfulfilled: the digital persona concept, two decades later. Information Technology & People 27(2), 182–207
[4] Jagadish, H. et al. (2014). Big data and its technical challenges. Communications of the ACM 57(7), 86–94
[5] Boyd, D. & Crawford, K. (2012). Critical questions for big data. Information, Comm. & Society 15(5), 662–679. [22] Clarke, R. (2014). What drones inherit from their ancestors. Computer Law & Security Review 30(3), 247–262
[6] Amelec, V. (2015). Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy. Advanced Science Letters, 21(5), 1406-1408.
[8] Guo, P. (2013). Data science workflow: Overview and challenges. Communications of the ACM Blog.
[9] Ariza, P., Pineres, M., Santiago, L., Mercado, N., & De la Hoz, A. (2014, November). Implementation of moprosoft level I and II in software development companies in the colombian caribbean, a commitment to the software product quality region. In 2014 IEEE Central America and Panama Convention (CONCAPAN XXXIV) (pp. 1-5). IEEE.
[10] Lis-Gutiérrez JP., Lis-Gutiérrez M., Gaitán-Angulo M., Balaguera MI., Viloria A., Santander-Abril JE. (2018) Use of the Industrial Property System for New Creations in Colombia: A Departmental Analysis (2000–2016). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.
[11] Gaitán-Angulo M., Cubillos Díaz J., Viloria A., Lis-Gutiérrez JP., Rodríguez-Garnica P.A. (2018) Bibliometric Analysis of Social Innovation and Complexity (Databases Scopus and Dialnet 2007–2017). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.
[12] Jesus Silva, Jenny Cubillos, Jesus Vargas Villa, Ligia Romero, Darwin Solano, Claudia Fernández. (2019). Preservation of confidential information privacy and association rule hiding for data mining: a bibliometric review. Procedia Computer Science 151; 1219–1224.
[13] Adhvaryu R, Domadiya N (2012). An Improved EMHS Algorithm for Privacy Preserving in Association Rule Mining on Horizontally Partitioned Database. In: Security in Computing and Communications Springer Berlin Heidelberg, pp: 272-280.
[14] G Li, M Xi. An Improved Algorithm for Privacy-preserving Data Mining Based on NMF. In: Journal of Information & Computational Science, 12(9) (2015), pp. 3423-3430
[15] Lis-Gutiérrez J.P., Henao C., Zerda Á., Gaitán M., Correa J.C., Viloria A. (2018) Determinants of the Impact Factor of Publications: A Panel Model for Journals Indexed in Scopus 2017. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham
[16] Isasi, P., Galván, I.: Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson. ISBN 8420540250 (2004)
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spelling Silva, JesusPineda Lezama, Omar BonergeRomero Marin, Ligia CieloSolano, DarwinSilva Fernández, Claudia Susana2020-02-05T13:28:32Z2020-02-05T13:28:32Z201900002010https://hdl.handle.net/11323/5989Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Today, as technologies mature and people are encouraged to contribute data to organizations’ databases, more transactions are being captured than ever before. Meanwhile, improvements in data storage technologies have made the cost of evaluating, selecting, and destroying legacy data considerably greater than simply letting it accumulate. On the one hand, the excess of stored data has considerably increased the opportunities to interrelate and analyze them, while the moderate enthusiasm generated by data warehousing and data mining in the 1990s has been replaced by a rampant euphoria about big data and data analytics. But, is this as wonderful as seems? This paper presents a risk analysis of Big Data and Big Data Analytics based on a review of quality factors.Hoy, a medida que las tecnologías maduran y se alienta a las personas a contribuir con datos a las bases de datos de las organizaciones, se capturan más transacciones que nunca. Mientras tanto, las mejoras en las tecnologías de almacenamiento de datos han hecho que el costo de evaluar, seleccionar y destruir datos heredados sea considerablemente mayor que simplemente dejar que se acumulen. Por un lado, el exceso de datos almacenados ha aumentado considerablemente las oportunidades para interrelacionarlos y analizarlos, mientras que el entusiasmo moderado generado por el almacenamiento de datos y la minería de datos en la década de 1990 ha sido reemplazado por una euforia desenfrenada sobre big data y análisis de datos. Pero, ¿es esto tan maravilloso como parece? Este documento presenta un análisis de riesgos de Big Data y Big Data Analytics basado en una revisión de factores de calidad.Silva, JesusPineda Lezama, Omar BonergeRomero Marin, Ligia Cielo-will be generated-orcid-0000-0002-1216-4489-600Solano, Darwin-will be generated-orcid-0000-0001-8996-0953-600Silva Fernández, Claudia Susana-will be generated-orcid-0000-0002-1931-2720-600engProcedia Computer ScienceCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Data managementData qualityDecision makingData analysisGestión de datosCalidad de datosToma de decisionesAnálisis de datosRisk analysis of using big data in computer sciencesAnálisis de riesgos del uso de big data en ciencias de la computaciónArtículo 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] Schroeck, M. et al. (2012). Analytics: The real-world use of big data. IBM Institute for Business Value. University of Oxford.[2] McAfee, A. & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Rev. 90, 61–68.[3] Clarke, R. (2014). Promise unfulfilled: the digital persona concept, two decades later. Information Technology & People 27(2), 182–207[4] Jagadish, H. et al. (2014). Big data and its technical challenges. Communications of the ACM 57(7), 86–94[5] Boyd, D. & Crawford, K. (2012). Critical questions for big data. Information, Comm. & Society 15(5), 662–679. [22] Clarke, R. (2014). What drones inherit from their ancestors. Computer Law & Security Review 30(3), 247–262[6] Amelec, V. (2015). Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy. Advanced Science Letters, 21(5), 1406-1408.[8] Guo, P. (2013). Data science workflow: Overview and challenges. Communications of the ACM Blog.[9] Ariza, P., Pineres, M., Santiago, L., Mercado, N., & De la Hoz, A. (2014, November). Implementation of moprosoft level I and II in software development companies in the colombian caribbean, a commitment to the software product quality region. In 2014 IEEE Central America and Panama Convention (CONCAPAN XXXIV) (pp. 1-5). IEEE.[10] Lis-Gutiérrez JP., Lis-Gutiérrez M., Gaitán-Angulo M., Balaguera MI., Viloria A., Santander-Abril JE. (2018) Use of the Industrial Property System for New Creations in Colombia: A Departmental Analysis (2000–2016). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.[11] Gaitán-Angulo M., Cubillos Díaz J., Viloria A., Lis-Gutiérrez JP., Rodríguez-Garnica P.A. (2018) Bibliometric Analysis of Social Innovation and Complexity (Databases Scopus and Dialnet 2007–2017). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.[12] Jesus Silva, Jenny Cubillos, Jesus Vargas Villa, Ligia Romero, Darwin Solano, Claudia Fernández. (2019). Preservation of confidential information privacy and association rule hiding for data mining: a bibliometric review. Procedia Computer Science 151; 1219–1224.[13] Adhvaryu R, Domadiya N (2012). An Improved EMHS Algorithm for Privacy Preserving in Association Rule Mining on Horizontally Partitioned Database. In: Security in Computing and Communications Springer Berlin Heidelberg, pp: 272-280.[14] G Li, M Xi. An Improved Algorithm for Privacy-preserving Data Mining Based on NMF. In: Journal of Information & Computational Science, 12(9) (2015), pp. 3423-3430[15] Lis-Gutiérrez J.P., Henao C., Zerda Á., Gaitán M., Correa J.C., Viloria A. (2018) Determinants of the Impact Factor of Publications: A Panel Model for Journals Indexed in Scopus 2017. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham[16] Isasi, P., Galván, I.: Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson. ISBN 8420540250 (2004)PublicationORIGINALRisk Analysis of Using Big Data in Computer Sciences.pdfRisk Analysis of Using Big Data in Computer Sciences.pdfapplication/pdf414937https://repositorio.cuc.edu.co/bitstreams/a7b96711-6246-4a3f-8527-127df9fe367a/download5ba73c31723500043c294743b47f6655MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/957ecf56-b3b9-42d5-94e0-81cbd3a0e5f2/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/c9a89198-8096-48f5-b71c-1bb8289f58ff/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILRisk Analysis of Using Big Data in Computer Sciences.pdf.jpgRisk Analysis of Using Big Data in Computer Sciences.pdf.jpgimage/jpeg45053https://repositorio.cuc.edu.co/bitstreams/62134039-2539-47ef-8128-680f65f32f5d/download3330b3a34d2629fa7420fcf35e2954d8MD55TEXTRisk Analysis of Using Big Data in Computer Sciences.pdf.txtRisk Analysis of Using Big Data in Computer Sciences.pdf.txttext/plain52240https://repositorio.cuc.edu.co/bitstreams/efd31f7e-8538-40b6-bf46-5ecf75b97260/downloadea3992d9b0b9f420b5b23567ccc2d821MD5611323/5989oai:repositorio.cuc.edu.co:11323/59892024-09-17 14:14:59.897http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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