Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing

[EN] Organizations, companies and start-ups need to cope with constant changes on the market which are difficult to predict. Therefore, the development of new systems to detect significant future changes is vital to make correct decisions in an organization and to discover new opportunities. A system based on business intelligence techniques is proposed to detect weak signals, that are related to future transcendental changes. While most known solutions are based on the use of structured data, the proposed system quantitatively detects these signals using heterogeneous and unstructured information from scientific, journalistic and social sources, applying text mining to analyze the documents and natural language processing to extract accurate results. The main contributions are that the system has been designed for any field, using different input datasets of documents, and with an automatic classification of categories for the detected keywords. In this research paper, results from the future of remote sensors are presented. Remote sensing services are providing new applications in observation and analysis of information remotely. This market is projected to witness a significant growth due to the increasing demand for services in commercial and defense industries. The system has obtained promising results, evaluated with two different methodologies, to help experts in the decision-making process and to discover new trends and opportunities.This research is partially supported by EIT Climate-KIC of the European Institute of Technology (project EIT Climate-KIC Accelerator-TC_3.1.5_190607_P066-1A) and InnoCENS from Erasmus + (573965-EPP-1-2016-1-SE-EPPKA2-CBHE-JP).Griol-Barres, I.; Milla, S.; Cebrián Ferriols, AJ.; Fan, H.; Millet Roig, J. (2020). Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing. Sustainability. 12(19):1-21. https://doi.org/10.3390/su12197848S1211219Zahra, S. A., Gedajlovic, E., Neubaum, D. O., & Shulman, J. M. (2009). A typology of social entrepreneurs: Motives, search processes and ethical challenges. Journal of Business Venturing, 24(5), 519-532. doi:10.1016/j.jbusvent.2008.04.007Ansoff, H. I. (1975). Managing Strategic Surprise by Response to Weak Signals. California Management Review, 18(2), 21-33. doi:10.2307/41164635Report on Weak Signals Collection. TELMAP, European Commission Seventh Framework Project (IST-257822) https://cordis.europa.eu/docs/projects/cnect/2/257822/080/deliverables/001-D41Weaksignalscollectionfinal.docDator, J. (2005). Universities without «quality» and quality without «universities». On the Horizon, 13(4), 199-215. doi:10.1108/10748120510627321Hiltunen, E. (2008). The future sign and its three dimensions. Futures, 40(3), 247-260. doi:10.1016/j.futures.2007.08.021Thorleuchter, 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.2014.02.046Julien, P.-A., Andriambeloson, E., & Ramangalahy, C. (2004). Networks, weak signals and technological innovations among SMEs in the land-based transportation equipment sector. Entrepreneurship & Regional Development, 16(4), 251-269. doi:10.1080/0898562042000263249Xindong Wu, Xingquan Zhu, Gong-Qing Wu, & Wei Ding. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97-107. doi:10.1109/tkde.2013.109Koivisto, 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.2015.12.010Davis, J., & Groves, C. (2019). City/future in the making: Masterplanning London’s Olympic legacy as anticipatory assemblage. Futures, 109, 13-23. doi:10.1016/j.futures.2019.04.002Irvine, N., Nugent, C., Zhang, S., Wang, H., & NG, W. W. Y. (2019). Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments. Sensors, 20(1), 216. doi:10.3390/s20010216Huang, M., & Liu, Z. (2019). Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data. Sensors, 20(1), 6. doi:10.3390/s20010006Awan, F. M., Saleem, Y., Minerva, R., & Crespi, N. (2020). A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction. Sensors, 20(1), 322. doi:10.3390/s20010322MohamadiBaghmolaei, 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-170720McGrath, J., & Fischetti, J. (2019). What if compulsory schooling was a 21st century invention? Weak signals from a systematic review of the literature. International Journal of Educational Research, 95, 212-226. doi:10.1016/j.ijer.2019.02.006Chao, W., Jiang, X., Luo, Z., Hu, Y., & Ma, W. (2019). Interpretable Charge Prediction for Criminal Cases with Dynamic Rationale Attention. Journal of Artificial Intelligence Research, 66, 743-764. doi:10.1613/jair.1.11377Van Veen, B. L., Roland Ortt, J., & Badke-Schaub, P. G. (2019). Compensating for perceptual filters in weak signal assessments. Futures, 108, 1-11. doi:10.1016/j.futures.2019.02.018Thorleuchter, D., & Van den Poel, D. (2015). Idea mining for web-based weak signal detection. Futures, 66, 25-34. doi:10.1016/j.futures.2014.12.007Rowe, E., Wright, G., & Derbyshire, J. (2017). Enhancing horizon scanning by utilizing pre-developed scenarios: Analysis of current practice and specification of a process improvement to aid the identification of important ‘weak signals’. Technological Forecasting and Social Change, 125, 224-235. doi:10.1016/j.techfore.2017.08.001Yoon, 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.2012.04.059Yoo, S., & Won, D. (2018). Simulation of Weak Signals of Nanotechnology Innovation in Complex System. Sustainability, 10(2), 486. doi:10.3390/su10020486Suh, J. (2018). Generating Future-Oriented Energy Policies and Technologies from the Multidisciplinary Group Discussions by Text-Mining-Based Identification of Topics and Experts. Sustainability, 10(10), 3709. doi:10.3390/su10103709Kwon, L.-N., Park, J.-H., Moon, Y.-H., Lee, B., Shin, Y., & Kim, Y.-K. (2018). Weak signal detecting of industry convergence using information of products and services of global listed companies - focusing on growth engine industry in South Korea -. Journal of Open Innovation: Technology, Market, and Complexity, 4(1). doi:10.1186/s40852-018-0083-6Ben-Porat, O., Hirsch, S., Kuchi, L., Elad, G., Reichart, R., & Tennenholtz, M. (2020). Predicting Strategic Behavior from Free Text. Journal of Artificial Intelligence Research, 68. doi:10.1613/jair.1.11849Fink, 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.2016.03.009Ilmola, 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.2005.12.019Doulamis, N. D., Doulamis, A. D., Kokkinos, P., & Varvarigos, E. M. (2016). Event Detection in Twitter Microblogging. IEEE Transactions on Cybernetics, 46(12), 2810-2824. doi:10.1109/tcyb.2015.2489841Atefeh, F., & Khreich, W. (2013). A Survey of Techniques for Event Detection in Twitter. Computational Intelligence, 31(1), 132-164. doi:10.1111/coin.12017Mehmood, N. Q., Culmone, R., & Mostarda, L. (2017). Modeling temporal aspects of sensor data for MongoDB NoSQL database. Journal of Big Data, 4(1). doi:10.1186/s40537-017-0068-5Bjeladinovic, S. (2018). A fresh approach for hybrid SQL/NoSQL database design based on data structuredness. Enterprise Information Systems, 12(8-9), 1202-1220. doi:10.1080/17517575.2018.1446102Čerešňák, R., & Kvet, M. (2019). Comparison of query performance in relational a non-relation databases. Transportation Research Procedia, 40, 170-177. doi:10.1016/j.trpro.2019.07.027Yangui, 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.2016.08.138Willett, P. (2006). The Porter stemming algorithm: then and now. Program, 40(3), 219-223. doi:10.1108/00330330610681295Griol-Barres, I., Milla, S., & Millet, J. (2019). Implementación de un sistema de detección de señales débiles de futuro mediante técnicas de minería de textos. Revista española de Documentación Científica, 42(2), 234. doi:10.3989/redc.2019.2.1599Kim, 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.2016.03.043Mendonça, S., Pina e Cunha, M., Kaivo-oja, J., & Ruff, F. (2004). Wild cards, weak signals and organisational improvisation. Futures, 36(2), 201-218. doi:10.1016/s0016-3287(03)00148-4Ishikiriyama, 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.2015.07.044Yuen, J. (2018). Comparison of Impact Factor, Eigenfactor Metrics, and SCImago Journal Rank Indicator and h-index for Neurosurgical and Spinal Surgical Journals. World Neurosurgery, 119, e328-e337. doi:10.1016/j.wneu.2018.07.144Thomason, J., Padmakumar, A., Sinapov, J., Walker, N., Jiang, Y., Yedidsion, H., … Mooney, R. (2020). Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog. Journal of Artificial Intelligence Research, 67, 327-374. doi:10.1613/jair.1.11485Tseng, 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.2006.11.011Gergelova, M. B., Labant, S., Kuzevic, S., Kuzevicova, Z., & Pavolova, H. (2020). Identification of Roof Surfaces from LiDAR Cloud Points by GIS Tools: A Case Study of Lučenec, Slovakia. Sustainability, 12(17), 6847. doi:10.3390/su12176847Badmos, O., Rienow, A., Callo-Concha, D., Greve, K., & Jürgens, C. (2018). Urban Development in West Africa—Monitoring and Intensity Analysis of Slum Growth in Lagos: Linking Pattern and Process. Remote Sensing, 10(7), 1044. doi:10.3390/rs10071044Thomson, E., Malhi, Y., Bartholomeus, H., Oliveras, I., Gvozdevaite, A., Peprah, T., … Doughty, C. (2018). Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery. Remote Sensing, 10(10), 1532. doi:10.3390/rs10101532Samasse, K., Hanan, N., Tappan, G., & Diallo, Y. (2018). Assessing Cropland Area in West Africa for Agricultural Yield Analysis. Remote Sensing, 10(11), 1785. doi:10.3390/rs10111785Anchang, J. Y., Prihodko, L., Kaptué, A. T., Ross, C. W., Ji, W., Kumar, S. S., … Hanan, N. P. (2019). Trends in Woody and Herbaceous Vegetation in the Savannas of West Africa. Remote Sensing, 11(5), 576. doi:10.3390/rs11050576Jung, H. C., Getirana, A., Arsenault, K. R., Holmes, T. R. H., & McNally, A. (2019). Uncertainties in Evapotranspiration Estimates over West Africa. Remote Sensing, 11(8), 892. doi:10.3390/rs11080892Mondal, P., Liu, X., Fatoyinbo, T. E., & Lagomasino, D. (2019). Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa. Remote Sensing, 11(24), 2928. doi:10.3390/rs11242928Meftah, M., Damé, L., Keckhut, P., Bekki, S., Sarkissian, A., Hauchecorne, A., … Bui, A. (2019). UVSQ-SAT, a Pathfinder CubeSat Mission for Observing Essential Climate Variables. Remote Sensing, 12(1), 92. doi:10.3390/rs12010092Zhang, 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.2008.03.044Griol-Barres, I., Milla, S., & Millet, J. (2020). Improving strategic decision making by the detection of weak signals in heterogeneous documents by text mining techniques. AI Communications, 32(5-6), 347-360. doi:10.3233/aic-190625Dzedzickis, A., Kaklauskas, A., & Bucinskas, V. (2020). Human Emotion Recognition: Review of Sensors and Methods. Sensors, 20(3), 592. doi:10.3390/s20030592Haegeman, 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.2012.10.002Silva, V. O., Martins, C. A. P. S., & Ekel, P. Y. (2018). An Efficient Parallel Implementation of an Optimized Simplex Method in GPU-CUDA. IEEE Latin America Transactions, 16(2), 564-573. doi:10.1109/tla.2018.832741

Predict the emergence - Application to competencies in job offers

International audience—Predicting the emergence of an event enables to anticipate and make decisions upstream. For instance, in the employment sector, it becomes necessary to anticipate the emergence of competencies requirements to help job seekers, education and training organization to better match the needs of the job market. Several approaches address the competencies mining with ontologies, we adopt a different point of view by using pattern mining. We propose a new methodology to predict emerging patterns and apply it to competencies with a dataset of job offers collected on the Web. Our model allows to identify potential emerging pattern over time and thus enables to take decisions accordingly

Modelling of a System for the Detection of Weak Signals Through Text Mining and NLP. Proposal of Improvement by a Quantum Variational Circuit

Tesis por compendio[ES] En esta tesis doctoral se propone y evalúa un sistema para detectar señales débiles (weak signals) relacionadas con cambios futuros trascendentales. Si bien la mayoría de las soluciones conocidas se basan en el uso de datos estructurados, el sistema propuesto detecta cuantitativamente estas señales utilizando información heterogénea y no estructurada de fuentes científicas, periodísticas y de redes sociales. La predicción de nuevas tendencias en un medio tiene muchas aplicaciones. Por ejemplo, empresas y startups se enfrentan a cambios constantes en sus mercados que son muy difíciles de predecir. Por esta razón, el desarrollo de sistemas para detectar automáticamente cambios futuros significativos en una etapa temprana es relevante para que cualquier organización tome decisiones acertadas a tiempo. Este trabajo ha sido diseñado para obtener señales débiles del futuro en cualquier campo dependiendo únicamente del conjunto de datos de entrada de documentos. Se aplican técnicas de minería de textos y procesamiento del lenguaje natural para procesar todos estos documentos. Como resultado, se obtiene un mapa con un ranking de términos, una lista de palabras clave clasificadas automáticamente y una lista de expresiones formadas por múltiples palabras. El sistema completo se ha probado en cuatro sectores diferentes: paneles solares, inteligencia artificial, sensores remotos e imágenes médicas. Este trabajo ha obtenido resultados prometedores, evaluados con dos metodologías diferentes. Como resultado, el sistema ha sido capaz de detectar de forma satisfactoria nuevas tendencias en etapas muy tempranas que se han vuelto cada vez más importantes en la actualidad. La computación cuántica es un nuevo paradigma para una multitud de aplicaciones informáticas. En esta tesis doctoral también se presenta un estudio de las tecnologías disponibles en la actualidad para la implementación física de qubits y puertas cuánticas, estableciendo sus principales ventajas y desventajas, y los marcos disponibles para la programación e implementación de circuitos cuánticos. Con el fin de mejorar la efectividad del sistema, se describe un diseño de un circuito cuántico basado en máquinas de vectores de soporte (SVM) para la resolución de problemas de clasificación. Este circuito está especialmente diseñado para los ruidosos procesadores cuánticos de escala intermedia (NISQ) que están disponibles actualmente. Como experimento, el circuito ha sido probado en un computador cuántico real basado en qubits superconductores por IBM como una mejora para el subsistema de minería de texto en la detección de señales débiles. Los resultados obtenidos con el experimento cuántico muestran también conclusiones interesantes y una mejora en el rendimiento de cerca del 20% sobre los sistemas convencionales, pero a su vez confirman que aún se requiere un desarrollo tecnológico continuo para aprovechar al máximo la computación cuántica.[CA] En aquesta tesi doctoral es proposa i avalua un sistema per detectar senyals febles (weak signals) relacionats amb canvis futurs transcendentals. Si bé la majoria de solucions conegudes es basen en l'ús de dades estructurades, el sistema proposat detecta quantitativament aquests senyals utilitzant informació heterogènia i no estructurada de fonts científiques, periodístiques i de xarxes socials. La predicció de noves tendències en un medi té moltes aplicacions. Per exemple, empreses i startups s'enfronten a canvis constants als seus mercats que són molt difícils de predir. Per això, el desenvolupament de sistemes per detectar automàticament canvis futurs significatius en una etapa primerenca és rellevant perquè les organitzacions prenguen decisions encertades a temps. Aquest treball ha estat dissenyat per obtenir senyals febles del futur a qualsevol camp depenent únicament del conjunt de dades d'entrada de documents. S'hi apliquen tècniques de mineria de textos i processament del llenguatge natural per processar tots aquests documents. Com a resultat, s'obté un mapa amb un rànquing de termes, un llistat de paraules clau classificades automàticament i un llistat d'expressions formades per múltiples paraules. El sistema complet s'ha provat en quatre sectors diferents: panells solars, intel·ligència artificial, sensors remots i imatges mèdiques. Aquest treball ha obtingut resultats prometedors, avaluats amb dues metodologies diferents. Com a resultat, el sistema ha estat capaç de detectar de manera satisfactòria noves tendències en etapes molt primerenques que s'han tornat cada cop més importants actualment. La computació quàntica és un paradigma nou per a una multitud d'aplicacions informàtiques. En aquesta tesi doctoral també es presenta un estudi de les tecnologies disponibles actualment per a la implementació física de qubits i portes quàntiques, establint-ne els principals avantatges i desavantatges, i els marcs disponibles per a la programació i implementació de circuits quàntics. Per tal de millorar l'efectivitat del sistema, es descriu un disseny d'un circuit quàntic basat en màquines de vectors de suport (SVM) per resoldre problemes de classificació. Aquest circuit està dissenyat especialment per als sorollosos processadors quàntics d'escala intermèdia (NISQ) que estan disponibles actualment. Com a experiment, el circuit ha estat provat en un ordinador quàntic real basat en qubits superconductors per IBM com una millora per al subsistema de mineria de text. Els resultats obtinguts amb l'experiment quàntic també mostren conclusions interessants i una millora en el rendiment de prop del 20% sobre els sistemes convencionals, però a la vegada confirmen que encara es requereix un desenvolupament tecnològic continu per aprofitar al màxim la computació quàntica.[EN] In this doctoral thesis, a system to detect weak signals related to future transcendental changes is proposed and tested. While most known solutions are based on the use of structured data, the proposed system quantitatively detects these signals using heterogeneous and unstructured information from scientific, journalistic, and social sources. Predicting new trends in an environment has many applications. For instance, companies and startups face constant changes in their markets that are very difficult to predict. For this reason, developing systems to automatically detect significant future changes at an early stage is relevant for any organization to make right decisions on time. This work has been designed to obtain weak signals of the future in any field depending only on the input dataset of documents. Text mining and natural language processing techniques are applied to process all these documents. As a result, a map of ranked terms, a list of automatically classified keywords and a list of multi-word expressions are obtained. The overall system has been tested in four different sectors: solar panels, artificial intelligence, remote sensing, and medical imaging. This work has obtained promising results that have been evaluated with two different methodologies. As a result, the system was able to successfully detect new trends at a very early stage that have become more and more important today. Quantum computing is a new paradigm for a multitude of computing applications. This doctoral thesis also presents a study of the technologies that are currently available for the physical implementation of qubits and quantum gates, establishing their main advantages and disadvantages and the available frameworks for programming and implementing quantum circuits. In order to improve the effectiveness of the system, a design of a quantum circuit based on support vector machines (SVMs) is described for the resolution of classification problems. This circuit is specially designed for the noisy intermediate-scale quantum (NISQ) computers that are currently available. As an experiment, the circuit has been tested on a real quantum computer based on superconducting qubits by IBM as an improvement for the text mining subsystem in the detection of weak signals. The results obtained with the quantum experiment show interesting outcomes with an improvement of close to 20% better performance than conventional systems, but also confirm that ongoing technological development is still required to take full advantage of quantum computing.Griol Barres, I. (2022). Modelling of a System for the Detection of Weak Signals Through Text Mining and NLP. Proposal of Improvement by a Quantum Variational Circuit [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/183029TESISCompendi

Aplicação da metodologia de Horizon Scanning no âmbito da inovação numa empresa energética

Mestrado Bolonha em Economia e Gestão de Ciência, Tecnologia e InovaçãoO presente TFM descreve um relatório de estágio no qual o estagiário relata a sua experiência numa empresa portuguesa do setor da energia. O objetivo principal do estágio centrou-se na aplicação da metodologia de Horizon Scanning (HS) no âmbito da Inovação dessa empresa, (tendo sido implementada pela primeira vez no contexto desta organização). O processo implementado de HS consistiu em explorar e identificar diferentes Forças de Mudança (Megatendências, Tendências, Sinais Fracos e Wild cards), permitindo criar, de forma participativa e em colaboração com vários stakeholders internos de diferentes áreas da organização, Scanning Dashboard/Radares de Tendências que poderão ter impacto nas atividades da organização, utilizando para esse efeito a plataforma digital da FIBRES®. Desta forma, foi possível utilizar esses insights para explorar impactos, opções e oportunidades em áreas da organização. Além disso, tendo por base esses resultados, foram apresentadas e recomendadas propostas de possíveis temas e tendências a observar no médio/longo prazo pela área de Inovação. Acredita-se que este projeto ajudará a organização a perceber a potencialidade e aplicação da metodologia de Horizon Scanning, como parte integrante da Prospetiva, que permita explorar o futuro de forma estruturada e sistemática. Por fim, este trabalho pode contribuir com insights para o documento estratégico de inovação da organização, elaborado pela equipa da área de InovaçãoThis TFM describes an internship report in which the trainee reports his internship experience in a Portuguese company in the energy sector. Thus, the main objective of the internship focused on the application of the Horizon Scanning (HS) methodology within the scope of Innovation in that company, having been implemented for the first time within the context of this organization. The implemented HS process consisted of exploring and identifying different forces of change (Megatrends, Trends, Weak Signals and Wild cards), allowing the creation, in a participatory way and in collaboration with several internal stakeholders from different areas of the organization, Scanning Dashboard/Radars of Trends that will have a strong impact on the organization's activities, using the FIBRES® digital platform for this purpose. In this way, it was possible to use these insights to explore impacts, options and opportunities in areas of the organization. In addition, based on these results, proposals were presented and recommended for possible themes and trends to be observed in the medium/long term by the Innovation area. It’s believed that this project will help the organization to realize the potential and application of the Horizon Scanning methodology, as an integral part of Foresight, which will allow exploring the future in a structured and systematic way. Finally, this work can contribute with insights to the organization's innovation strategy document, prepared by the Innovation area team.info:eu-repo/semantics/publishedVersio

Recovering the divide : a review of the big data analytics—strategy relationship

Research on big data analytics has been burgeoning in recent decades, yet its relationship with strategy continues to be overlooked. This paper reviews how big data analytics and strategy are portrayed across 228 articles, identifying two dominant discourses: an input-output discourse that views big data analytics as a computational capability supplementing prospective strategy formulation and an entanglement discourse that theorizes big data analytics as a socially constructed agent that (re)shapes the emergent character of strategy formation. We deconstruct the inherent dichotomies of the input-output/entanglement divide and reveal how both discourses adopt disjointed positions vis-à-vis relational causality and agency. We elaborate a semiotic view of big data analytics and strategy that transcends this standoff and provides a novel theoretical account for conjoined relationality between big data analytics and strategy

Inteligência estratégica antecipativa : identificação de sinais fracos por meio do Big Data Analytics

Com o ambiente empresarial cada vez mais dinâmico e complexo, seu monitoramento tem sido uma prática obrigatória para as organizações. Dado que nas últimas décadas há um volume importante de informações disponibilizadas por tecnologias digitais, organizações estão investindo em ferramentas Big Data Analytics (BDA) com o propósito de compreender o mercado e reduzir a incerteza na tomada de decisão. Ao considerar esse tipo de dados nos processos de monitoramento, analistas possivelmente fortalecem a identificação de Sinais Fracos (sinais antecipativos). Acreditando que tais processos possam ser guiados por dados, buscou-se investigar a seguinte questão de pesquisa: qual o impacto das práticas do BDA na identificação de Sinais Fracos? Objetiva-se, portanto analisar a relação entre o uso de práticas do BDA e a identificação de Sinais Fracos, fonte de informações da Inteligência Antecipativa. Neste intuito, aproximando as disciplinas da área de Sistemas de Informação - Analytics e Inteligência Antecipativa - realizou-se duas pesquisas empíricas, a primeira de natureza qualitativa e a segunda quantitativa. Com o método de entrevistas em profundidade, a primeira pesquisa objetiva compreender se os gestores percebem Sinais Fracos por meio das ferramentas BDA, e seu uso potencial na tomada de decisão estratégica. Foram reveladas práticas do BDA que influenciam o monitoramento e a identificação de Sinais Fracos. Apresentou-se evidências de que dados candidatos a Sinais Fracos podem ser identificados a partir do BDA e de que essas ferramentas facilitam o monitoramento do ambiente. Na segunda pesquisa, a fim de medir o efeito das práticas do BDA na identificação de Sinais Fracos, desenvolveu-se hipóteses relacionadas em um modelo conceitual. Para suportá-lo, elaborou-se uma pesquisa do tipo survey, com 123 respondentes. As respostas foram analisadas mediante modelagem de equações estruturais (PLS-SEM), suportando o modelo e resultando em achados relevantes, indicando que o uso das práticas do BDA tem efeito positivo na identificação de Sinais Fracos. Como contribuições teóricas destaca-se o suporte empírico ao modelo desenvolvido, demonstrando a importância das variáveis que têm impacto significativo na identificação de sinais fracos.As the business environment is increasingly becoming dynamic and complex, its monitoring has been a mandatory practice for organizations. Given that in the last decades there is an important volume of information available, organizations are investing in Big Data Analytics (BDA) tools, with the purpose of understanding the market and reducing the uncertainty in decision making. Considering this type of data in the monitoring processes, analysts possibly strengthen the identification of weak signals. Believing that such processes could be data-driven, we sought to investigate the following research question: what is the impact of BDA's practices in the identification of weak signs? Thus, the aim of this study is to analyze the relationship between the use of BDA practices and the identification of weak signs, an information source from Anticipative Intelligence. To this end, bringing together the Information Systems area disciplines - Analytics and Anticipative Intelligence - two empirical types of research were carried out, the first had a qualitative nature and the second had a quantitative nature. With the method of in-depth interviews, the first research aims to understand whether managers perceive weak signals through the BDA tools, and their potential use in strategic decision-making. BDA practices that influence the monitoring and identification of weak signals were revealed. Evidence was presented that candidate data for weak signals can be identified by BDA. In the second research, in order to measure the effect of BDA practices in the identification of weak signals, related hypotheses were developed in a conceptual model. To support it, a survey was conducted, with 123 respondents. The responses were analyzed using structural equation modeling (PLS-SEM), supporting the model and resulting in relevant findings, indicating that the use of BDA practices has a positive effect on identifying weak signals. As theoretical contributions, it stands out the empirical support of the model, demonstrating the importance of variables that have a significant impact on the identification of weak signals

A hybrid machine learning and text-mining approach for the automated generation of early warnings in construction project management.

The thesis develops an early warning prediction methodology for project failure prediction by analysing unstructured project documentation. Project management documents contain certain subtle aspects that directly affect or contribute to various Key Performance Indicators (KPIs). Extracting actionable outcomes as early warnings (EWs) from management documents (e.g. minutes and project reports) to prevent or minimise discontinuities such as delays, shortages or amendments is a challenging process. These EWs, if modelled properly, may inform the project planners and managers in advance of any impending risks. At presents, there are no suitable machine learning techniques to benchmark the identification of such EWs in construction management documents. Extraction of semantically crucial information is a challenging task which is reflected substantially as teams communicate via various project management documents. Realisation of various hidden signals from these documents in without a human interpreter is a challenging task due to the highly ambiguous nature of language used and can in turn be used to provide decision support to optimise a project’s goals by pre-emptively warning teams. Following up on the research gap, this work develops a “weak signal” classification methodology from management documents via a two-tier machine learning model. The first-tier model exploits the capability of a probabilistic Naïve Bayes classifier to extract early warnings from construction management text data. In the first step, a database corpus is prepared via a qualitative analysis of expertly-fed questionnaire responses that indicate relationships between various words and their mappings to EW classes. The second-tier model uses a Hybrid Naïve Bayes classifier which evaluates real-world construction management documents to identify the probabilistic relationship of various words used against certain EW classes and compare them with the KPIs. The work also reports on a supervised K-Nearest-Neighbour (KNN) TF-IDF methodology to cluster and model various “weak signals” based on their impact on the KPIs. The Hybrid Naïve Bayes classifier was trained on a set of documents labelled based on expertly-guided and indicated keyword categories. The overall accuracy obtained via a 5-fold cross-validation test was 68.5% which improved to 71.5% for a class-reduced (6-class) KNN-analysis. The Weak Signal analysis of the same dataset generated an overall accuracy of 64%. The results were further analysed with Jack-Knife resembling and showed consistent accuracies of 65.15%, 71.42% and 64.1% respectively.PhD in Manufacturin

Semantic weak signal tracing

The weak signal concept according to Ansoff has the aim to advance strategic early warning. It enables to predict the appearance of events in advance that are relevant for an organization. An example is to predict the appearance of a new and relevant technology for a research organization. Existing approaches detect weak signals based on an environmental scanning procedure that considers textual information from the internet. This is because about 80% of all data in the internet are textual information. The texts are processed by a specific clustering approach where clusters that represent weak signals are identified. In contrast to these related approaches, we propose a new methodology that investigates a sequence of clusters measured at successive points in time. This enables to trace the development of weak signals over time and thus, it enables to identify relevant weak signal developments for organizations decision making in strategic early warning environment

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

[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 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.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).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-190625S347360325-6Ansoff, H. I. (1975). Managing Strategic Surprise by Response to Weak Signals. California Management Review, 18(2), 21-33. doi:10.2307/41164635S. 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:10.1108/10748120510627321Duan, 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.2010.06.067Fink, 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.2016.03.009Guralnik, 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:10.1145/312129.312190Haegeman, 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.2012.10.002Hiltunen, E. (2008). The future sign and its three dimensions. Futures, 40(3), 247-260. doi:10.1016/j.futures.2007.08.021Ilmola, 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.2005.12.019Ishikiriyama, 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.2015.07.044K. 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:10.1137/1.9781611972795.34Kim, 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.2016.03.043Koivisto, 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.2015.12.010Liu, 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.2013.01.012MohamadiBaghmolaei, 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-170720Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523. doi:10.1016/0306-4573(88)90021-0Schoemaker, 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.2012.10.020Thorleuchter, 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.2014.02.046Thorleuchter, D., & Van den Poel, D. (2015). Idea mining for web-based weak signal detection. Futures, 66, 25-34. doi:10.1016/j.futures.2014.12.007Tseng, 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.2006.11.011Willett, P. (2006). The Porter stemming algorithm: then and now. Program, 40(3), 219-223. doi:10.1108/00330330610681295Yangui, 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.2016.08.138Yoon, 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.2012.04.059Zhang, 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.2008.03.04