Dirk Meissner, National Research University Higher School of Economics, Moscow, Russia, firstname.lastname@example.org
Ilya Kuzminov, National Research University Higher School of Economics, Moscow, Russia, email@example.com
David Sarpong, Brunel University (David.Sarpong@brunel.ac.uk)
Landscape mapping studies, including trend spotting, new technologies identification, actor mapping and centres of competence benchmarking, historically had been an integral part, not explicitly positioned, of various strategic studies aimed at stating the current conditions and prospects of development of countries and regions, sectors of economy and domains of knowledge. With the progress of strategic analytical studies as a specific activity meeting needs of high-level – and increasingly often distributed multilevel – decision-makers, foresight studies, including Future oriented technology analysis (Cagnin et al. 2008) and Science and Technology (S&T) Foresight (ForSTI) (Miles et al. 2016), emerged as a special framework of such research, with various mapping activities being formally named and methodologically scoped within. This task is especially relevant for the purpose of identifying the prospects for human capital development. In this regard, the identification of promising areas of S&T that can make a significant contribution to the advancement of human potential requires constant scanning, monitoring and analysis of the processes taking place in this area.
However, the growing gap between the needs and capacities of strategic analytics is becoming increasingly apparent. Strategic management systems at the state and corporate levels experience a growing need of timely and objective information (evidence-based policy), summarised to the state where it could be transformed into specific management decisions (actionable intelligence). At the same time, the fact that traditional institutional and organisational mechanisms, such as hierarchical systems of specialised centres of competence (research institutes, analytical centres, consulting companies), are unable to meet this need, becomes more and more transparent. If we look at the scale, generality, and negative consequences of this trend, we may see that the management system of social, economic and S&T spheres suffers a growing data intelligence crisis.
This trend is caused by the unprecedented increase in volumes of significant and relevant information (Reinsel et al., 2018) on the backdrop of digitalisation of the global economy. The increase in the volume of information that is valuable and relevant for decision-making is associated not only with the growth in the ability of information systems to record and reflect a greater number of phenomena in economics, technology and other spheres. It is also conditioned by the structural complexity of these areas, diversification, emergence of a greater number of parallel development processes, where different teams, organisations and other centres of competence are participating in parallel innovation activities.
Consequently, basic natural limitations of the abilities of the human brain to process and remember information require automation and augmentation of strategic analytics by natural language processing technologies, in particular text-mining (primary processing of large unstructured text arrays) and semantic analysis (semantic structuring of pre-processed text data). The most significant potential in this area is associated with processing of large text-data and their metadata (natural language processing, NLP). In recent years, NLP has experienced a technological revolution (Saravia, 2018) due to the development of deep learning and the emergence of embedding models that assign each stable letter combination, a word, a language term or even a document a high dimension numerical vector, thereby allowing to predict an element by its context or a context by its element, calculate the semantic proximity of terms and documents, perform “semantic arithmetic operations”. At the same time, new generations of multilayer embedding models appear – still very large in volume and computationally complex, but they radically expand the NLP capabilities. We are talking about such models and approaches as BERT, ULMfit, GPT-2, ELMo, which encode the meaning of words and add new, generative capabilities (filling in the gaps in text, writing augmented text on the initial fragment, answers to questions with explanation of the choice) to the capabilities of traditional embedding models (such as word2vec, GloVe, fastText). The development of NLP and automatic machine learning (AutoML) applications in NLP leads to a significant decrease in the dependence on wide-scale expert labour. Meanwhile, even embedding models and neural networks in NLP tasks do not yet allow to automatically generate accurate subject taxonomies and ontologies; they do not produce the results (especially in expert tasks) that are achievable on the basis of expensive ontologically-controlled methods (in a manner of speaking, “automatically generated on-demand expert systems” are not yet possible). Nevertheless, every day in global scientific and business communities, steps are being taken in this direction, which it is important to track in order not to miss new opportunities for application in strategic analytics.
Special Issue’s scope, including potential themes to be addressed in the Special Issue
Driven by the growing interest in new instruments for fact-based decision-making, we propose to organize a special issue aimed to place a scholarly call for academic endeavour to provide an elective and comprehensive knowledge that elaborates on application of big data analysis for trend spotting. We solicit for papers from scholars who are actively working in this broad area of enquiry to recommend approaches that strengthen the existing analytical practice and provide fresh insights that would set the roadmap for further enquiries. Contributions may address, but are not limited to, the topics listed in the following:• Expert methods in strategic studies: limitations and potential for renewed role• Psychophysiological and organisational limitations of humans and human groups in information processing: meaning for strategic studies and big data applications• Big Data, Data Integration, and Machine Learning technologies for raising completeness, representativeness, and unbiasedness of strategic studies’ results• Approaches for using Big Data, Open Data, Citizen Data Marketplaces, Small Data, Data APIs for trend spotting, finding insights and weak signals in strategic studies• Natural language processing technologies, semantics, text mining and big documentary data as a key data tool for strategic studies• New ways of enhancement and augmentation of data organisation and analysis methods based on NLP and ML • Applications of language models, advanced NLP and hybrid ML tools in practical tasks of technology strategic studies, future studies, foresight exercises and trend analysis in research of economic sectors and markets• Applications of digital tools and big data technologies including NLP in sociological studies, digital sociology, and studies of human potential and human capital in modern society
Notes for Prospective Authors
Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Conference papers may only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper.Manuscripts should be submitted through the publisher’s online system. Submissions will be reviewed according to the journal’s rigorous standards and procedures through a double-blind peer review by at least two qualified reviewers.
Please prepare the manuscript according to IEEE-TEM’s guidelines (http://ieee-tmc.org/tem-guidelines) and submit to the journal’s Manuscript Central site (https://mc.manuscriptcentral.com/tem-ieee). Please upload the paper on the IEEE TEM Editorial Manager clearly indicating in the cover letter that the submission is for the IEEE TEM Special Issue on Application of Big Data Analysis for Trend Spotting related to the Development and Use of Human Capital.
Papers submitted by December 31, 2022
References and key literature related to the motivation and focus of the proposed issue
Cagnin, C., Keenan, M., Johnston, R., Scapolo, F., & Barre, R. (2008) Future-oriented technology analysis. Strategic Intelligence for an Innovative Economy. Springer-Verlag Berlin Heidelberg.
Miles, I., Saritas, O., & Sokolov, A. (2016) Foresight for science, technology and innovation. Springer International Publishing
Reinsel D., Gantz J., Rydning J. (2018) The Digitization of the World. From Edge to Core. IDC White Paper I Doc# US44413318 I November 2018. https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf
Saravia E. (2018) NLP 2018 Highlights. http://elvissaravia.com/nlp-highlights-2018/
Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information processing & management, 24(5), 513-523.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
Van Rijsbergen, C. J. (1977). A theoretical basis for the use of co‐occurrence data in information retrieval. Journal of documentation, 33 (2), 106-119. https://doi.org/10.1108/eb026637
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural language processing (almost) from scratch. Journal of machine learning research, 12, 2493-2537.
Turian, J., Ratinov, L., & Bengio, Y. (2010, July). Word representations: a simple and general method for semi-supervised learning. In Proceedings of the 48th annual meeting of the association for computational linguistics (pp. 384-394).
Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. science, 290(5500), 2323-2326.
Ando, R. K., Zhang, T., & Bartlett, P. (2005). A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 6(11).
Zhou, Y., Dong, F., Liu, Y., Li, Z., Du, J. F., & Zhang, L. (2020). Forecasting emerging technologies using data augmentation and deep learning. Scientometrics, 123(1). https://doi.org/10.1007/s11192-020-03351-6
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507. https://doi.org/10.1126/science.1127647
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Khan, Z., & Vorley, T. (2017). Big data text analytics: an enabler of knowledge management. Journal of Knowledge Management, 21(1), 18–34. https://doi.org/10.1108/JKM-06-2015-0238
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly: Management Information Systems, 36(4), 1165–1188. https://doi.org/10.2307/41703503
Randhawa, K., Wilden, R., & Hohberger, J. (2016). A bibliometric review of open innovation: Setting a research agenda. Journal of Product Innovation Management, 33(6), 750-772.
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6), 734-749.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286.
Zhang, Y., Zhang, G., Chen, H., Porter, A. L., Zhu, D., & Lu, J. (2016). Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research. Technological Forecasting and Social Change, 105, 179-191.
Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information sciences, 275, 314-347.
Grover, V., Chiang, R. H., Liang, T. P., & Zhang, D. (2018). Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems, 35(2), 388-423.
Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547-578.
Kim, G. H., Trimi, S., & Chung, J. H. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78-85.
Günther, W. A., Mehrizi, M. H. R., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems, 26(3), 191-209.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., … & Darrell, T. (2014, November). Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 675-678).
Grover, A., & Leskovec, J. (2016, August). node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 855-864).
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015, May). Line: Large-scale information network embedding. In Proceedings of the 24th international conference on world wide web (pp. 1067-1077).
Karpathy, A., & Fei-Fei, L. (2015). Deep visual-semantic alignments for generating image descriptions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3128-3137).
Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440).
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichletallocation. the Journal of machine Learning research, 3, 993-1022.
Van Der Maaten, L. (2014). Accelerating t-SNE using tree-based algorithms. The Journal of Machine Learning Research, 15(1), 3221-3245.
Bose, R. (2009). Advanced analytics: opportunities and challenges. Industrial Management & Data Systems.
Howard, J., & Ruder, S. (2018). Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146.
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