scholarly journals Big Data: paradigm in construction in the face of the challenges and challenges of the financial sector in the 21st century

2021 ◽  
Vol 25 (110) ◽  
pp. 127-137
Author(s):  
Sonia Tigua Moreira ◽  
Edison Cruz Navarrete ◽  
Geovanny Cordova Perez
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Big Data Analytics ◽  
Financial Sector ◽  
Database Systems ◽  
Predictive Capacity ◽  
Data Clusters ◽  
Sixth International Conference ◽  
The Face ◽  
Sector Decision

The world of finance is immersed in multiple controversies, laden with contradictions and uncertainties typical of a social ecosystem, generating dynamic changes that lead to significant transformations, where the thematic discussion of Big Data becomes crucial for real-time logical decision-making. In this field of knowledge is located this article, which reports as a general objective to explore the strengths, weaknesses and future trends of Big Data in the financial sector, using as a methodology for exploration a scientific approach with the bibliographic tools scopus and scielo, using as a search equation the Big Data, delimited to the financial sector. The findings showed the growing importance of gaining knowledge from the huge amount of financial data generated daily globally, developing predictive capacity towards creating scenarios inclined to find solutions and make timely decisions. Keywords: Big Data, financial sector, decision-making. References [1]D. Reinsel, J. Gantz y J. Rydning, «Data Age 2025: The Evolution of Data to Life-Critical,» IDC White Pape, 2017. [2]R. Barranco Fragoso, «Que es big data IBM Developer works,» 18 Junio 2012. [Online]. Available: https://developer.ibm.com/es/articles/que-es-big-data/. [3]IBM, «IBM What is big data? - Bringing big data to the enterprise,» 2014. [Online]. Available: http://www.ibm.com/big-data/us/en/. [4]IDC, «Resumen Ejecutivo -Big Data: Un mercado emergente.,» Junio 2012. [Online]. Available: https://www.diarioabierto.es/wp-content/uploads/2012/06/Resumen-Ejecutivo-IDC-Big-Data.pdf. [5]Factor humano Formación, «Factor humano formación escuela internacional de postgrado.,» 2014. [Online]. Available: http//factorhumanoformación.com/big-data-ii/. [6]J. Luna, «Las tecnologías Big Data,» 23 Mayo 2018. [Online]. Available: https://www.teldat.com/blog/es/procesado-de-big-data-base-de-datos-de-big-data-clusters-nosql-mapreduce/#:~:text=Tecnolog%C3%ADas%20de%20procesamiento%20Big%20Data&text=De%20este%20modo%20es%20posible,las%20necesidades%20de%20procesado%20disminuyan. [7]T.A.S Foundation, "Apache cassandra 2015", The apache cassandra project, 2015. [8]E. Dede, B. Sendir, P. Kuzlu, J. Hartog y M. Govindaraju, «"An Evaluation of Cassandra for Hadoop",» de 2013 IEEE Sixth International Conference on Cloud Computing, Santa Clara, CA, USA, 2013. [9]The Apache Software Foundation, «"Apache HBase",» 04 Agosto 2017. [Online]. Available: http://hbase.apache.org/. [10]G. Deka, «"A Survey of Cloud Database Systems",» IT Professional, vol. 16, nº 02, pp. 50-57, 2014. [11]P. Dueñas, «Introducción al sistema financiero y bancario,» Bogotá. Politécnico Grancolombiano, 2008. [12]V. Mesén Figueroa, «Contabilización de CONTRATOS de FUTUROS, OPCIONES, FORWARDS y SWAPS,» Tec Empresarial, vol. 4, nº 1, pp. 42-48, 2010. [13] A. Castillo, «Cripto educación es lo que se necesita para entender el mundo de la Cripto-Alfabetización,» Noticias Artech Digital , 04 Junio 2018. [Online].Available: https://www.artechdigital.net/cripto-educacion-cripto-alfabetizacion/. [14]Conceptodefinicion.de, «Definicion de Cienciometría,» 16 Diciembre 2020. [Online]. Available: https://conceptodefinicion.de/cienciometria/. [15]Elsevier, «Scopus The Largest database of peer-reviewed literature» https//www.elsevier.com/solutions/scopus., 2016. [16]J. Russell, «Obtención de indicadores bibliométricos a partir de la utilización de las herramientas tradicionales de información,» de Conferencia presentada en el Congreso Internacional de información-INFO 2004, La Habana, Cuba, 2004. [17]J. Durán, Industrialized and Ready for Digital Transformation?, Barcelona: IESE Business School, 2015. [18]P. Orellana, «Omnicanalidad,» 06 Julio 2020. [Online]. Available: https://economipedia.com/definiciones/omnicanalidad.html. [19]G. Electrics, «Innovation Barometer,» 2018. [20]D. Chicoma y F. Casafranca, Interviewees, Entrevista a Daniel Chicoma y Fernando Casafranca, docentes del PADE Internacional en Gerencia de Tecnologías de la Información en ESAN. [Entrevista]. 2018. [21]L.R. La república, «La importancia del mercadeo en la actualidad,» 21 Junio 2013. [Online]. Available: https://www.larepublica.co/opinion/analistas/la-importancia-del-mercadeo-en-la-actualidad-2041232#:~:text=El%20mercadeo%20es%20cada%20d%C3%ADa,en%20los%20mercados%20(clientes). [22]UNED, «Acumulación de datos y Big data: Las preguntas correctas,» 10 Noviembre 2017. [Online]. Available: https://www.masterbigdataonline.com/index.php/en-el-blog/150-el-big-data-y-las-preguntas-correctas. [23]J. García, Banca aburrida: el negocio bancario tras la crisis económica, Fundacion Funcas - economía y sociedad, 2015, pp. 101 - 150. [24]G. Cutipa, «Las 5 principales ventajas y desventajas de bases de datos relacionales y no relacionales: NoSQL vs SQL,» 20 Abril 2020. [Online]. Available: https://guidocutipa.blog.bo/principales-ventajas-desventajas-bases-de-datos-relacionales-no-relacionales-nosql-vs-sql/. [25]R. Martinez, «Jornadas Big Data ANALYTICS,»19 Septiembre 2019. [Online]. Available: https://www.cfp.upv.es/formacion-permanente/curso/jornada-big-data-analytics_67010.html. [26]J. Rifkin, The End of Work: The Decline of the Global Labor Force and the Dawn of the Post-Market Era, Putnam Publishing Group, 1995. [27]R. Conde del Pozo, «Los 5 desafíos a los que se enfrenta el Big Data,» 13 Agosto 2019. [Online]. Available: https://diarioti.com/los-5-desafios-a-los-que-se-enfrenta-el-big-data/110607.

Examining the potential of textual big data analytics for public policy decision-making: A case study with driverless cars in Denmark

2020 ◽  
Vol 98 ◽  
pp. 68-78 ◽  
Author(s):  
Aseem Kinra ◽  
Samaneh Beheshti-Kashi ◽  
Rasmus Buch ◽  
Thomas Alexander Sick Nielsen ◽  
Francisco Pereira
Keyword(s):  
Public Policy ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Policy Decision ◽  
Driverless Cars ◽  
Policy Decision Making

Semantic Technologies and Big Data Analytics for Cyber Defence

Web Services ◽  
2019 ◽  
pp. 1430-1443
Author(s):  
Louise Leenen ◽  
Thomas Meyer
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Data Sets ◽  
Semantic Technologies ◽  
Data Types ◽  
Intelligent Decision Making ◽  
Big Data Technologies

The Governments, military forces and other organisations responsible for cybersecurity deal with vast amounts of data that has to be understood in order to lead to intelligent decision making. Due to the vast amounts of information pertinent to cybersecurity, automation is required for processing and decision making, specifically to present advance warning of possible threats. The ability to detect patterns in vast data sets, and being able to understanding the significance of detected patterns are essential in the cyber defence domain. Big data technologies supported by semantic technologies can improve cybersecurity, and thus cyber defence by providing support for the processing and understanding of the huge amounts of information in the cyber environment. The term big data analytics refers to advanced analytic techniques such as machine learning, predictive analysis, and other intelligent processing techniques applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends and other useful information. Semantic technologies is a knowledge representation paradigm where the meaning of data is encoded separately from the data itself. The use of semantic technologies such as logic-based systems to support decision making is becoming increasingly popular. However, most automated systems are currently based on syntactic rules. These rules are generally not sophisticated enough to deal with the complexity of decisions required to be made. The incorporation of semantic information allows for increased understanding and sophistication in cyber defence systems. This paper argues that both big data analytics and semantic technologies are necessary to provide counter measures against cyber threats. An overview of the use of semantic technologies and big data technologies in cyber defence is provided, and important areas for future research in the combined domains are discussed.

Big Data Analytics

2018 ◽  
pp. 124-138
Author(s):  
Sreenu G. ◽  
M.A. Saleem Durai
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Real Life ◽  
Big Data Analytics ◽  
Speed Up ◽  
Processing Techniques ◽  
And Storage ◽  
Big Data Computing ◽  
New Algorithms ◽  
Over Time

Advances in recent hardware technology have permitted to document transactions and other pieces of information of everyday life at an express pace. In addition of speed up and storage capacity, real-life perceptions tend to transform over time. However, there are so much prospective and highly functional values unseen in the vast volume of data. For this kind of applications conventional data mining is not suitable, so they should be tuned and changed or designed with new algorithms. Big data computing is inflowing to the category of most hopeful technologies that shows the way to new ways of thinking and decision making. This epoch of big data helps users to take benefit out of all available data to gain more precise systematic results or determine latent information, and then make best possible decisions. Depiction from a broad set of workloads, the author establishes a set of classifying measures based on the storage architecture, processing types, processing techniques and the tools and technologies used.

Effective Statistical Methods for Big Data Analytics

2017 ◽  
pp. 280-299 ◽  
Author(s):  
Cheng Meng ◽  
Ye Wang ◽  
Xinlian Zhang ◽  
Abhyuday Mandal ◽  
Wenxuan Zhong ◽  
...  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Knowledge Discovery ◽  
Statistical Methods ◽  
Large Scale ◽  
Big Data Analytics ◽  
Divide And Conquer ◽  
Data Driven ◽  
The Past ◽  
Large Scale Dataset

With advances in technologies in the past decade, the amount of data generated and recorded has grown enormously in virtually all fields of industry and science. This extraordinary amount of data provides unprecedented opportunities for data-driven decision-making and knowledge discovery. However, the task of analyzing such large-scale dataset poses significant challenges and calls for innovative statistical methods specifically designed for faster speed and higher efficiency. In this chapter, we review currently available methods for big data, with a focus on the subsampling methods using statistical leveraging and divide and conquer methods.

A Conceptual Model for Describing the Integration of Decision Aspect into Big Data

2022 ◽  
pp. 294-318
Author(s):  
Fatma Chiheb ◽  
Fatima Boumahdi ◽  
Hafida Bouarfa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Competitive Advantage ◽  
Conceptual Model ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Modern Business ◽  
Definition Of ◽  
A Company ◽  
Support Decision Making

Big Data is an important topic for discussion and research. It has gained this importance due to the meaningful value that could be extracted from these data. The application of Big Data in the modern business allows enterprises to take faster and smarter decisions, achieving a real competitive advantage. However, a lot of Big Data projects provide disappointing results that don't address the decision-makers' needs due to many reasons. The main reason for this failure can be summarized in neglecting the study of the decision-making aspect of these projects. In light of this challenge, this study proposes the integration of decision aspect into Big Data as a solution. Therefore, this article presents three main contributions: 1) Clarify the definition of Big Data; 2) Presents BD-Da model, a conceptual model describes the levels that should be considered to develop a Big Data project aiming to solve a problem that calls a decision; 3) Describes a particular, logical, requirements-like approach that explains how a company develops a Big Data analytics project to support decision-making.

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