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Improving strategic decision making by the detection of weak signals in heterogeneous documents by text mining techniques

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Improving strategic decision making by the detection of weak signals in heterogeneous documents by text mining techniques

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Griol Barres, I.; Milla, S.; Millet Roig, J. (2019). Improving strategic decision making by the detection of weak signals in heterogeneous documents by text mining techniques. AI Communications. 32(5-6):347-360. https://doi.org/10.3233/AIC-190625

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/147520

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Metadatos del ítem

Título: Improving strategic decision making by the detection of weak signals in heterogeneous documents by text mining techniques
Autor: Griol Barres, Israel Milla, Sergio Millet Roig, José
Entidad UPV: Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica
Fecha difusión:
Resumen:
[EN] At present, one of the greatest threats to companies is not being able to cope with the constant changes that occur in the market because they do not predict them well in advance. Therefore, the development of new ...[+]
Palabras clave: Weak signal of the future , Strategic decision making , Text mining , Business intelligence architecture , Unstructured information
Derechos de uso: Cerrado
Fuente:
AI Communications. (issn: 0921-7126 )
DOI: 10.3233/AIC-190625
Editorial:
IOS Press
Versión del editor: https://doi.org/10.3233/AIC-190625
Código del Proyecto:
info:eu-repo/grantAgreement/EIT Climate-KIC//TC2018B-2.2.5-ACCUPV-P066-1A/
info:eu-repo/grantAgreement/EC/Erasmus+/573965-EPP-1-2016-1-SE-EPPKA2-CBHE-JP/EU/Enhancing innovation competences and entrepreneurial skills in engineering education/InnoCENS/
Agradecimientos:
This work is partially supported by EIT Climate KIC of the European Union (project Accelerator TC2018B-2.2.5-ACCUPV-P066-1A) and Erasmus+ InnoCENS (573965-EPP-1-2016-1-SE-EPPKA2-CBHE-JP).
Tipo: Artículo

References

Ansoff, H. I. (1975). Managing Strategic Surprise by Response to Weak Signals. California Management Review, 18(2), 21-33. doi:10.2307/41164635

S. Bird, E. Loper and E. Klein, Natural Language Processing with Python, O’Reilly Media Inc., 2009.

A. Cooper, C. Voigt, E. Unterfrauner, M. Kravcik, J. Pawlowski and H. Pirkkalainen, Report on weak signals collection, in: TELMAP, European Commission Seventh Framework Project (IST-257822), Deliverable D4.1, 2011, pp. 6–7. [+]
Ansoff, H. I. (1975). Managing Strategic Surprise by Response to Weak Signals. California Management Review, 18(2), 21-33. doi:10.2307/41164635

S. Bird, E. Loper and E. Klein, Natural Language Processing with Python, O’Reilly Media Inc., 2009.

A. Cooper, C. Voigt, E. Unterfrauner, M. Kravcik, J. Pawlowski and H. Pirkkalainen, Report on weak signals collection, in: TELMAP, European Commission Seventh Framework Project (IST-257822), Deliverable D4.1, 2011, pp. 6–7.

J. Dator, Futures studies as applied knowledge, in: New Thinking for a New Millennium, R. Slaughter, ed., Routledge, London, 1996, www.futures.hawaii.edu/dator/futures/appliedknow.html.

Dator, J. (2005). Universities without «quality» and quality without «universities». On the Horizon, 13(4), 199-215. doi:10.1108/10748120510627321

Duan, J., Zhang, M., Jingzhong, W., & Xu, Y. (2011). A hybrid framework to extract bilingual multiword expression from free text. Expert Systems with Applications, 38(1), 314-320. doi:10.1016/j.eswa.2010.06.067

Fink, L., Yogev, N., & Even, A. (2017). Business intelligence and organizational learning: An empirical investigation of value creation processes. Information & Management, 54(1), 38-56. doi:10.1016/j.im.2016.03.009

Guralnik, V., & Srivastava, J. (1999). Event detection from time series data. Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’99. doi:10.1145/312129.312190

Haegeman, K., Marinelli, E., Scapolo, F., Ricci, A., & Sokolov, A. (2013). Quantitative and qualitative approaches in Future-oriented Technology Analysis (FTA): From combination to integration? Technological Forecasting and Social Change, 80(3), 386-397. doi:10.1016/j.techfore.2012.10.002

Hiltunen, E. (2008). The future sign and its three dimensions. Futures, 40(3), 247-260. doi: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. doi:10.1016/j.futures.2005.12.019

Ishikiriyama, C. S., Miro, D., & Gomes, C. F. S. (2015). Text Mining Business Intelligence: A small sample of what words can say. Procedia Computer Science, 55, 261-267. doi:10.1016/j.procs.2015.07.044

K. Jung, A Study of Foresight Method Based on Text Mining and Complexity Network Analysis, KISTEP, Seoul, 2010.

Kawahara, Y., & Sugiyama, M. (2009). Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation. Proceedings of the 2009 SIAM International Conference on Data Mining. doi:10.1137/1.9781611972795.34

Kim, J., Han, M., Lee, Y., & Park, Y. (2016). Futuristic data-driven scenario building: Incorporating text mining and fuzzy association rule mining into fuzzy cognitive map. Expert Systems with Applications, 57, 311-323. doi:10.1016/j.eswa.2016.03.043

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. Technological Forecasting and Social Change, 104, 180-190. doi:10.1016/j.techfore.2015.12.010

Liu, S., Yamada, M., Collier, N., & Sugiyama, M. (2013). Change-point detection in time-series data by relative density-ratio estimation. Neural Networks, 43, 72-83. doi:10.1016/j.neunet.2013.01.012

MohamadiBaghmolaei, R., Mozafari, N., & Hamzeh, A. (2017). Continuous states latency aware influence maximization in social networks. AI Communications, 30(2), 99-116. doi:10.3233/aic-170720

Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523. doi:10.1016/0306-4573(88)90021-0

Schoemaker, P. J. H., Day, G. S., & Snyder, S. A. (2013). Integrating organizational networks, weak signals, strategic radars and scenario planning. Technological Forecasting and Social Change, 80(4), 815-824. doi:10.1016/j.techfore.2012.10.020

Thorleuchter, D., Scheja, T., & Van den Poel, D. (2014). Semantic weak signal tracing. Expert Systems with Applications, 41(11), 5009-5016. doi:10.1016/j.eswa.2014.02.046

Thorleuchter, D., & Van den Poel, D. (2015). Idea mining for web-based weak signal detection. Futures, 66, 25-34. doi:10.1016/j.futures.2014.12.007

Tseng, Y.-H., Lin, C.-J., & Lin, Y.-I. (2007). Text mining techniques for patent analysis. Information Processing & Management, 43(5), 1216-1247. doi:10.1016/j.ipm.2006.11.011

Willett, P. (2006). The Porter stemming algorithm: then and now. Program, 40(3), 219-223. doi:10.1108/00330330610681295

Yangui, R., Nabli, A., & Gargouri, F. (2016). Automatic Transformation of Data Warehouse Schema to NoSQL Data Base: Comparative Study. Procedia Computer Science, 96, 255-264. doi:10.1016/j.procs.2016.08.138

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. doi:10.1016/j.eswa.2012.04.059

Zhang, W., Yoshida, T., & Tang, X. (2008). Text classification based on multi-word with support vector machine. Knowledge-Based Systems, 21(8), 879-886. doi:10.1016/j.knosys.2008.03.044

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