Design and development of a custom system of technology surveillance and competitive intelligence in SMEs
Date
2019
2019
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Abstract
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.
Referencias:
[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.
[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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