System implementation for detection of future weak signals using text mining
DOI:
https://doi.org/10.3989/redc.2019.2.1599Keywords:
weak signal of the future, business intelligence architecture, unstructured information, text mining, decision-makingAbstract
Nowadays, one of the biggest threats for companies is not being able to cope with the constant changes occurring in the market by not predicting them well in advance. For this reason, the development of new processes that facilitate the detection of future phenomena and significant changes is a key component for correct decision making that can mark a correct course in the company. A business intelligence based architecture system is proposed to allow discrete changes or weak signals detection in the present that are indicative of more significant phenomena and transcendental changes in the future. In contrast with current available works, which are focused on structured information sources or, at most, with only a single type of data source, in this paper the detection of these signals is done quantitatively from various kinds of heterogeneous and unstructured documents (scientific articles, journalistic articles and social networks) on which text mining techniques are applied. The system has been tested in the study of the future of solar panels sector, obtaining promising results that can help business experts in the recognition of new driving factors of their markets and the development of new opportunities.
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References
Ansoff, H.I. (1975). Managing Strategic Surprise by Response to Weak Signals. California Management Review, 18 (2), 21-33. https://doi.org/10.2307/41164635
Ansoff, ?. I.; McDonnell, E. J. (1990). Implanting strategic management. Cambridge: Prentice Hall.
Beautiful Soup (2018). Beautiful Soup Documentation. Disponible en: https://www.crummy.com/software/BeautifulSoup/bs4/doc/ [Fecha de consulta: 1/09/2018].
Cembrero, I. (2011). El gigantesco proyecto solar del Sáhara abastecerá a España en 2015. El País. Disponible en: http://elpais.com/diario/2011/11/09/sociedad/1320793203_850215.html [Fecha de consulta: 1/09/2018].
Conesa-Caralt, J; Curto-Diaz, J. (2010). Introducción al Business Intelligence. Barcelona: Editorial UOC.
Connolly, T.; Begg, C. (2005). Database Systems: A Practical Approach to Design, Implementation, and Management (4th ed.). London: Addison-Wesley.
Cooke, R. (2015). África podría convertirse en la nueva esperanza para la producción de energía solar. Vice News. Disponible en: https://news-old-origin. vice.com/es/article/africa-convertirse-nueva-esperanza-produccion-energia-solar [Fecha de consulta: 1/09/2018].
Cooper, A.; Voigt, C.; Unterfrauner, E.; Kravcik, M.; Pawlowski, J.; Pirkkalainen, H. (2011). TELMAP. Report on Weak Signals Collection. Bolton: European Commission Seventh Framework Project (IST-257822).
Dator, J. (2005). Universities without quality and quality without universities. On the Horizon, 13 (4), 199-215. https://doi.org/10.1108/10748120510627321
Dedi?, N.; Stanier C. (2017). Measuring the Success of Changes to Existing Business Intelligence Solutions to Improve Business Intelligence Reporting. Journal of Management Analytics, 4 (2), 130-144. https://doi.org/10.1080/23270012.2017.1299048
Eisenhardt K.M.; Brown S.L. (1999). Patching: restitching business portfolios in dynamic markets. Harvard Business Review, 77 (3), 72-82.
Elcacho, J. (2014). Megaproyecto para llevar energía solar desde el Sáhara hasta Europa. La Vanguardia. Disponible en: http://www.lavanguardia.com/natural/20141022/54417391167/megaproyecto-tunur-energia-solar-electricidad-sahara-europa.html [Fecha de consulta: 1/09/2018].
Elmasri, R.; Navathe, S.B. (2011). Fundamentals of Database Systems (6th ed.). Atlanta: Addison-Wesley.
Finger, L.; Dutta, S. (2014). Ask, Measure, Learn: Using Social Media Analytics to Understand and Influence Customer Behavior. Sebastopol: O'Reilly Media.
Fischler, M. A.; Firschein, O. (1987). Intelligence: The Eye, The Brain and The Computer. Menlo Park: Addison-Wesley.
Giovinazzo, W. (2000). Object-Oriented Data Warehouse Design: Building a Star Schema. Santa Ana: Prentice- Hall.
Godet, M. (1994). From Anticipation to Action, A Handbook of Strategic Prospective. Paris: UNESCO Publishing.
Griol, D.; Patricio, M.A.; Molina, J.M. (2016). CALIMACO: desarrollo de un servicio de bibliotecario virtual para la interacción multimodal con dispositivos móviles. Revista Española de Documentación Científica, 39 (2), e129. https://doi.org/10.3989/redc.2016.2.1262
Han, J.; Kamber M; Pei, J. (2001). Data Mining: Concepts and Techniques. Waltham: Morgan Kaufmann Publishers.
Helsingin Sanomat. (2010). Hennes & Mauritz comenzará a comercializar ropa usada bajo la etiqueta de Vintage. Helsingin Sanomat.
Hernández, J.; Ramírez, M.J.; Ferri, C. (2004). Introducción a la minería de datos. Valencia: Pearson.
Hiltunen, E. (2008). The future sign and its three dimensions. Futures, 40 (3), 247-260. https://doi.org/10.1016/j.futures.2007.08.021
Ilmola, L.; Kuusi, O. (2006). Filters of weak signals hinder foresight: Monitoring weak signals efficiently in corporate decision-making. Futures, 38 (8), 908-924. https://doi.org/10.1016/j.futures.2005.12.019
Inmon, W.H. (2005). Building the Data Warehouse (4th ed.). Indianapolis: John Wiley.
Jung, K. (2010). A study of foresight method based on text mining and complexity network analysis. Seoul: KISTEP.
Khan, R.A. (2012). KDD for Business Intelligence. Journal of Knowledge Management Practice, 13 (2).
Kimball, R.; Ross, M. (2002). The Data Warehouse Toolkit: the complete guide to dimensional modelling. Indianapolis: John Wiley.
Koivisto, R.; Kulmala, I.; Gotcheva, N. (2016). Weak signals and damage scenarios. Systematics to identify weak signals and their sources related to mass transport attacks. Finland Technological Forecasting and Social Change 104, 180-190. https://doi.org/10.1016/j.techfore.2015.12.010
Kuusi, O.; Hiltunen, E. (2007). The Signification Process of the Future Sign. Turku: Finland Futures Research Centre ebook 4/2007.
Mannermaa M. (1999). Tulevaisuuden hallinta skenaariot strategiatyoskentelyssa. (Managing the future, Scenarios in strategy work). Provoo: WSOY.
MohamadiBaghmolaei, R.; Mozafari, N.; Hamzeh, A. (2017). Continuous states latency aware influence maximization in social networks. AI Communications, 30 (2), 99-116. https://doi.org/10.3233/AIC-170720
Molitor, G.T. (2003). Molitor Forecasting Model: Key Dimensions for Plotting the Patterns of Change. Journal of Future Studies, 8 (1), 61-72.
MongoDB (2018). Documentación de MongoDB. Disponible en: https://www.mongodb.com/es [Fecha de consulta: 1/09/2018].
Natural Language Toolkit (2018). NLTK 3.3 Documentation. Disponible en: https://www.nltk.org/ [Fecha de consulta: 1/09/2018].
New York Times (2018). New York Times. Disponible en: http://www.nytimes.com [Fecha de consulta: 1/09/2018].
Nikander, I.O. (2002). Early Warnings, A Phenomenon in Project Management, Dissertation for the degree of Doctor of Science in Technology. Helsinki: Helsinki University of Technology.
Peirce, C.S. (1868). Some Consequences of Four Incapacities. Journal of Speculative Philosophy, 2 (3), 140- 157. https://www.jstor.org/stable/i25665647
Salton, G.; Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523. https://doi.org/10.1016/0306-4573(88)90021-0
ScienceDirect (2018). Science Direct. Disponible en: http://www.sciencedirect.com/ [Fecha de consulta: 1/09/2018].
Siegel, E. (2013). Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. New Jersey: John Wiley.
Twitter (2018a). Twitter. Disponible en: http://www. twitter.com [Fecha de consulta: 1/09/2018].
Twitter (2018b). Twitter API Documentation. Disponible en: https://dev.twitter.com/rest/public [Fecha de consulta: 1/09/2018].
UNESCO World Heritage Centre (2008). List of factors affecting the properties. Disponible en: http:// whc.unesco.org/en/factors/ [Fecha de consulta: 1/09/2018].
Witten, I.H.; Frank E. (2005). Data Mining: Practical Machine Learning Tools and Techniques (2nd ed.). San Francisco: The Morgan Kaufmann Series in Data Management Systems.
Yoo, S.H.; Park, H.W.; Kim, K.H. (2009). A study on exploring weak signals of technology innovation using informetrics. Journal of Technology Innovation, 17(2), 109-130.
Yoon, J. (2012). Detecting weak signals for long-term business opportunities using text mining of Web news. Expert Systems with Applications, 39 (16), 12543- 12550. https://doi.org/10.1016/j.eswa.2012.04.059
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