Sage Journals: Discover world-class research

Abstract

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 processes that facilitate the detection of significant phenomena and future changes is a key component for correct decision making that sets a correct course in the company. For this reason, a business intelligence architecture system is hereby proposed to allow the detection of discrete changes or weak signals in the present, indicative of more significant phenomena and transcendental changes in the future. In contrast to work currently available focusing on structured information sources, or at most with a single type of data source, the detection of these signals is here quantitatively based on heterogeneous and unstructured documents of various kinds (scientific journals, newspaper articles and social networks), to which text mining and natural language processing techniques (a multi-word expression analysis) are applied. The system has been tested to study the future of the artificial intelligence sector, obtaining promising results to help business experts in the recognition of new driving factors of their markets and the development of new opportunities.

Keywords

Weak signal of the future strategic decision making text mining business intelligence architecture unstructured information

Get full access to this article

View all access options for this article.

References

H.I.

Ansoff, Managing strategic surprise by response to weak signals, California Management Review 18(2) (1975), 21–33. doi:10.2307/41164635.

Bird,

Loper and

Klein, Natural Language Processing with Python, O’Reilly Media Inc., 2009.

Brown and

Eisenhardt, Competing on the Edge: Strategy as Structured Chaos, Harvard Business School Press, Boston, 1998, p. 23.

Cooper,

Voigt,

Unterfrauner,

Kravcik,

Pawlowski and

Pirkkalainen, Report on weak signals collection, in: TELMAP, European Commission Seventh Framework Project (IST-257822), Deliverable D4.1, 2011, pp. 6–7.

Dator, Futures studies as applied knowledge, in: New Thinking for a New Millennium,

Slaughter , ed., Routledge, London, 1996, www.futures.hawaii.edu/dator/futures/appliedknow.html .

Dator, Universities without quality and quality without universities, On the Horizon 13(4) (2005), 199–215, www.futures.hawaii.edu/dator/education/DatorOzQual.htm . doi:10.1108/10748120510627321.

Duan,

Zhang,

Jingzhong and

Xu, A hybrid framework to extract bilingual multiword expression from free text, Expert Systems With Applications 38(1) (2011), 314–320. doi:10.1016/j.eswa.2010.06.067.

Eckhoff,

Markus,

Lassnig and

Schon, Detecting weak signals with technologies: Overview of current technology enhanced approaches for the detection of weak signals, Int. J. Trends Econ. Manag. Technol. 3 (2014), 1–7.

Fink,

Yogev and

Even, Business intelligence and organizational learning: An empirical investigation of value creation processes, Information and Management 54(1) (2017), 38–56. doi:10.1016/j.im.2016.03.009.

10.

M.A.

Fischler and

Firschein, Intelligence: The Eye, the Brain and the Computer, Addison-Wesley, 1987, p. 221.

11.

Godet, From Anticipation to Action, a Handbook of Strategic Prospective, UNESCO Publishing, 1994, p. 59.

12.

Griol-Barres,

Milla and

Roig, DoV + DoD in artificial intelligence weak signals detection, Figshare (2019), Dataset. doi:10.6084/m9.figshare.7583228.v2.

13.

Guralnik and

Srivastava, Event detection from time series data, in: KDD ’99 Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, California, USA, 1999, pp. 33–42. doi:10.1145/312129.312190.

14.

Haegeman,

Marinelli,

Scapolo,

Ricci and

Sokolov, Quantitative and qualitative approaches in future-oriented technology analysis (FTA): From combination to integration, Technol. Forecast. Soc. Chang. 80 (2013), 386–397. doi:10.1016/j.techfore.2012.10.002.

15.

Han and

Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, USA, 2001, p. 236.

16.

Hiltunen, The future sign and its three dimensions, Futures 40(3) (2008), 247–260. doi:10.1016/j.futures.2007.08.021.

17.

Ilmola and

Kuusi, Filters of weak signals hinder foresight: Monitoring weak signals efficiently in corporate decision-making, Futures 38 (2006), 908–924. doi:10.1016/j.futures.2005.12.019.

18.

W.H.

Inmon, Building the Data Warehouse, 4th edn, John Wiley, 2005, p. 156.

19.

C.S.

Ishikiriyama,

Miro and

C.F.

Simoes Gomesa, Text mining business intelligence: A small sample of what words can say, Procedia Computer Science 55 (2015), 261–267. doi:10.1016/j.procs.2015.07.044.

20.

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

21.

Kawahara and

Sugiyama, Change-point detection in time-series data by direct density-ratio estimation, in: Proceedings of the 2009 SIAM International Conference on Data Mining, 2009, pp. 389–400. doi:10.1137/1.9781611972795.34.

22.

R.A.

Khan, KDD for business intelligence, Journal of Knowledge Management Practice 13(2) (2012), 134.

23.

Kim,

Han,

Lee and

Park, Futuristic data-driven scenario building: Incorporating text mining and fuzzy association rule mining into fuzzy cognitive map, Expert Systems With Applications 57 (2006), 311–323. doi:10.1016/j.eswa.2016.03.043.

24.

Koivisto,

Kulmala and

Gotcheva, 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 (2016), 180–190. doi:10.1016/j.techfore.2015.12.010.

25.

Kuusi and

Hiltunen, The Signification Process of the Future Sign, Finland Futures Research Centre ebook 4/2007, 2007, p. 7.

26.

Liu,

Yamada,

Collier and

Sugiyama, Change-point detection in time-series data by relative density-ratio estimation, Neural Networks 43 (2013), 72–83. doi:10.1016/j.neunet.2013.01.012.

27.

Mannermaa, Tulevaisuuden Hallinta Skenaariot Strategiatyoskentelyssa (Managing the Future, Scenarios in Strategy Work), WSOY, Porvoo, 1999, p. 227.

28.

Mendonca,

M.P.

Cunha,

Kaivo-Oja and

Ruff, Wild cards, weak signals and organizational improvisation, FEUNL Working Paper No. 432, 2003.

29.

MohamadiBaghmolaei,

Mozafari and

Hamzeh, Continuous states latency aware influence maximization in social networks, AI Communications 30(2) (2017), 99–116. doi:10.3233/AIC-170720.

30.

G.T.

Molitor, Molitor forecasting model: Key dimensions for plotting the patterns of change, Journal of Future Studies 8(1) (2003), 61–72.

31.

I.O.

Nikander, Early warnings, a phenomenon in project management, Dissertation for the degree of Doctor of Science in Technology, Helsinki University of Technology, 2002, p. 196.

32.

C.S.

Peirce, Some consequences of four incapacities, Journal of Speculative Philosophy 2 (1868), 140–157, https://www.jstor.org/stable/i25665647 .

33.

Salton and

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

34.

P.J.H.

Schoemaker,

G.S.

Day and

S.A.

Snyder, Integrating organizational networks, weak signals, strategic radars and scenario planning, Technol. Forecast. Soc. Chang. 80 (2013), 815–824. doi:10.1016/j.techfore.2012.10.020.

35.

Thorleuchter,

Scheja and

Van den Poel, Semantic weak signal tracing, Expert Systems with Applications 41 (2014), 5009–5016. doi:10.1016/j.eswa.2014.02.046.

36.

Thorleuchter and

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

37.

Tsenga,

Lin and

Linc, Text mining techniques for patent analysis, Information Processing and Management 43(5) (2007), 1216–1247. doi:10.1016/j.ipm.2006.11.011.

38.

Willett, The Porter stemming algorithm: Then and now, Program: electronic library and information systems 40(3) (2006), 219–223. doi:10.1108/00330330610681295.

39.

Yangui,

Nabli and

Gargouri, Automatic transformation of data warehouse schema to NoSQL data base: Comparative study, Procedia Computer Science 96 (2016), 255–264. doi:10.1016/j.procs.2016.08.138.

40.

S.H.

Yoo,

H.W.

Park and

K.H.

Kim, A study on exploring weak signals of technology innovation using informetrics, Journal of Technology Innovation 17(2) (2009), 109–130.

41.

Yoon, Detecting weak signals for long-term business opportunities using text mining of web news, Expert Systems with Applications 39 (2012), 12543–12550. doi:10.1016/j.eswa.2012.04.059.

42.

Zhang,

Yoshida and

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

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

Abstract

Keywords

Get full access to this article

References