Design and development of a custom system of technology surveillance and competitive intelligence in SMEs

Making strategic decisions is a complex process that requires reliable and up-to-date information. It is therefore necessary to have tools that facilitate the information management. Technology Surveillance (TS) and Competitive Intelligence (CI) are two disciplines that seek to obtain accurate and u...

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Autores:
Silva, Jesus
Vidal Pacheco, Lucelys del Carmen
Parra Negrete, Kevin
Combita Niño, Johana Patricia
Pineda Lezama, Omar Bonerge
Izquierdo Varela, Noel
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:11323/4838
Acceso en línea:
http://hdl.handle.net/11323/4838
https://repositorio.cuc.edu.co/
Palabra clave:
Web mining
technology surveillance and competitive intelligence
decision making
advanced cluster vector page ranking
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
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repository_id_str
dc.title.spa.fl_str_mv Design and development of a custom system of technology surveillance and competitive intelligence in SMEs
title Design and development of a custom system of technology surveillance and competitive intelligence in SMEs
spellingShingle Design and development of a custom system of technology surveillance and competitive intelligence in SMEs
Web mining
technology surveillance and competitive intelligence
decision making
advanced cluster vector page ranking
title_short Design and development of a custom system of technology surveillance and competitive intelligence in SMEs
title_full Design and development of a custom system of technology surveillance and competitive intelligence in SMEs
title_fullStr Design and development of a custom system of technology surveillance and competitive intelligence in SMEs
title_full_unstemmed Design and development of a custom system of technology surveillance and competitive intelligence in SMEs
title_sort Design and development of a custom system of technology surveillance and competitive intelligence in SMEs
dc.creator.fl_str_mv Silva, Jesus
Vidal Pacheco, Lucelys del Carmen
Parra Negrete, Kevin
Combita Niño, Johana Patricia
Pineda Lezama, Omar Bonerge
Izquierdo Varela, Noel
dc.contributor.author.spa.fl_str_mv Silva, Jesus
Vidal Pacheco, Lucelys del Carmen
Parra Negrete, Kevin
Combita Niño, Johana Patricia
Pineda Lezama, Omar Bonerge
Izquierdo Varela, Noel
dc.subject.spa.fl_str_mv Web mining
technology surveillance and competitive intelligence
decision making
advanced cluster vector page ranking
topic Web mining
technology surveillance and competitive intelligence
decision making
advanced cluster vector page ranking
description Making strategic decisions is a complex process that requires reliable and up-to-date information. It is therefore necessary to have tools that facilitate the information management. Technology Surveillance (TS) and Competitive Intelligence (CI) are two disciplines that seek to obtain accurate and up-to-date information. Clearly, the web is the largest and most important source of information, but their destructuring and disorganization requires tools that help to manage it. This work presents a model for TS and CI using Web Mining techniques such as ranking algorithm of web pages based on machine learning, i.e. the Advanced Cluster Vector Page Ranking (ACVPR) algorithm.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-06-10T13:54:33Z
dc.date.available.none.fl_str_mv 2019-06-10T13:54:33Z
dc.date.issued.none.fl_str_mv 2019
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 0000-2010
dc.identifier.uri.spa.fl_str_mv http://hdl.handle.net/11323/4838
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 0000-2010
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url http://hdl.handle.net/11323/4838
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1] T. Hiltbrand; "Learning Competitive Intelligence From a Bunch of Screwballs", Business Intelligence Journal, vol: 15, no: 4, 2010. [2] Gaitán-Angulo M. Amelec Viloria, Jenny-Paola Lis-Gutiérrez, Dionicio Neira, Enrrique López, Ernesto Joaquín Steffens Sanabria, Claudia Patricia Fernández Castro. (2018) Influence of the Management of the Innovation in the Business Performance of the Family Business: Application to the Printing Sector in Colombia. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham. [3] A. Firat, W. Woon, and S. Madnick, “Technological Forecasting – A Review,” presented at the Working Paper CISL# 2008-15, Cambridge, 2008. [4] S. Madnick and W.L. Woon; Technology Forecasting Using Data Mining and Semantics, MIT/MIST Collaborative Research, 2009. [5] Adamopoulos, P., 2014. On discovering non-obvious recommendations: Using unexpectedness and neighborhood selection methods in collaborative filtering systems. Proceedings of the 7th ACM international conference on Web search and data mining, ACM, 655- 660. [6] Ahmad, M. W., Doja, M. N., & Ahmad, T., 2017. Enumerative feature subset based ranking system for learning to rank in presence of implicit user feedback. Journal of King Saud University-Computer and Information Sciences. Elsevier [7] R. Barainka; “Modelos de Vigilancia Tecnológica e Inteligencia Competitiva”. Servico Zaintek de BAI. 2006. [8] Xiang, B., Jiang, D., Pei, J., Sun, X., Chen, E., & Li, H., 2010. Context-aware ranking in web search. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, ACM, 451-458. [9] Yang, Y. F., Hwang, S. L., & Schenkman, B.,2012. An improved Web search engine for visually impaired users. Universal Access in the Information Society, 11(2), 113-124. [10] Zhu, H., Ou, C. X., Van den Heuvel, W. J. A. M., & Liu, H.,2017. Privacy calculus and its utility for personalization services in e- commerce: An analysis of consumer decision-making. Information & Management, Elsevier, 54(4), 427-437. [11] Zhou, D., Zhao, W., Wu, X., Lawless, S., & Liu, J., 2018. An iterative method for personalized results adaptation in cross-language search. Information Sciences, Elsevier, 430, 200-215. [12] Alam, M. and Sadaf, K., 2015. Labeling of Web Search Result Clusters using Heuristic Search and Frequent Itemset. Procedia Computer Science, Elsevier,216-222. [13] Ferretti, S., Mirri, S., Prandi, C., & Salomoni, P., 2016. Automatic web content personalization through reinforcement learning. Journal of Systems and Software, Elsevier, 121, 157-169. [14] I. Popa Anica and G. Cucui, "A Framework for Enhancing Competitive Intelligence Capabilities using Decision Support System based on Web Mining Techniques", Int. J. of Computers, Communications & Control, vol. 4, no. 4, pp. 326-334, 2009. [15] Malhotra, D., Malhotra, M. and Rishi, O.P., 2017.An Innovative Approach of Web Page Ranking Using Hadoop- and Map Reduce- Based Cloud Framework. Proceedings of Advances in Intelligent Systems and Computing, Vol.654, CSI, Springer, 421-427.
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spelling Silva, Jesus659ae35f3326439474c6cca46ee77cb0Vidal Pacheco, Lucelys del Carmend99bf5fba8b1c091b29d19ba76e315a2300Parra Negrete, Kevin59b26c8fc374bba308612a9c00085a11Combita Niño, Johana Patricia90128843279d20d297e7ee1c7ba2916aPineda Lezama, Omar Bonergee72941c91bdbbe143e36775e15fb92bdIzquierdo Varela, Noel62665fd2abfaa3e719ba56b0d69ac4bc3002019-06-10T13:54:33Z2019-06-10T13:54:33Z20190000-2010http://hdl.handle.net/11323/4838Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Making strategic decisions is a complex process that requires reliable and up-to-date information. It is therefore necessary to have tools that facilitate the information management. Technology Surveillance (TS) and Competitive Intelligence (CI) are two disciplines that seek to obtain accurate and up-to-date information. Clearly, the web is the largest and most important source of information, but their destructuring and disorganization requires tools that help to manage it. This work presents a model for TS and CI using Web Mining techniques such as ranking algorithm of web pages based on machine learning, i.e. the Advanced Cluster Vector Page Ranking (ACVPR) algorithm.engProcedia Computer ScienceAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Web miningtechnology surveillance and competitive intelligencedecision makingadvanced cluster vector page rankingDesign and development of a custom system of technology surveillance and competitive intelligence in SMEsArtículo 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] T. Hiltbrand; "Learning Competitive Intelligence From a Bunch of Screwballs", Business Intelligence Journal, vol: 15, no: 4, 2010. [2] Gaitán-Angulo M. Amelec Viloria, Jenny-Paola Lis-Gutiérrez, Dionicio Neira, Enrrique López, Ernesto Joaquín Steffens Sanabria, Claudia Patricia Fernández Castro. (2018) Influence of the Management of the Innovation in the Business Performance of the Family Business: Application to the Printing Sector in Colombia. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham. [3] A. Firat, W. Woon, and S. Madnick, “Technological Forecasting – A Review,” presented at the Working Paper CISL# 2008-15, Cambridge, 2008. [4] S. Madnick and W.L. Woon; Technology Forecasting Using Data Mining and Semantics, MIT/MIST Collaborative Research, 2009. [5] Adamopoulos, P., 2014. On discovering non-obvious recommendations: Using unexpectedness and neighborhood selection methods in collaborative filtering systems. Proceedings of the 7th ACM international conference on Web search and data mining, ACM, 655- 660. [6] Ahmad, M. W., Doja, M. N., & Ahmad, T., 2017. Enumerative feature subset based ranking system for learning to rank in presence of implicit user feedback. Journal of King Saud University-Computer and Information Sciences. Elsevier [7] R. Barainka; “Modelos de Vigilancia Tecnológica e Inteligencia Competitiva”. Servico Zaintek de BAI. 2006. [8] Xiang, B., Jiang, D., Pei, J., Sun, X., Chen, E., & Li, H., 2010. Context-aware ranking in web search. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, ACM, 451-458. [9] Yang, Y. F., Hwang, S. L., & Schenkman, B.,2012. An improved Web search engine for visually impaired users. Universal Access in the Information Society, 11(2), 113-124. [10] Zhu, H., Ou, C. X., Van den Heuvel, W. J. A. M., & Liu, H.,2017. Privacy calculus and its utility for personalization services in e- commerce: An analysis of consumer decision-making. Information & Management, Elsevier, 54(4), 427-437. [11] Zhou, D., Zhao, W., Wu, X., Lawless, S., & Liu, J., 2018. An iterative method for personalized results adaptation in cross-language search. Information Sciences, Elsevier, 430, 200-215. [12] Alam, M. and Sadaf, K., 2015. Labeling of Web Search Result Clusters using Heuristic Search and Frequent Itemset. Procedia Computer Science, Elsevier,216-222. [13] Ferretti, S., Mirri, S., Prandi, C., & Salomoni, P., 2016. Automatic web content personalization through reinforcement learning. Journal of Systems and Software, Elsevier, 121, 157-169. [14] I. Popa Anica and G. Cucui, "A Framework for Enhancing Competitive Intelligence Capabilities using Decision Support System based on Web Mining Techniques", Int. J. of Computers, Communications & Control, vol. 4, no. 4, pp. 326-334, 2009. [15] Malhotra, D., Malhotra, M. and Rishi, O.P., 2017.An Innovative Approach of Web Page Ranking Using Hadoop- and Map Reduce- Based Cloud Framework. 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