A background of the refined mathematical toolkit is the Multigrammatical Framework, developed for the assessment and optimization of the large-scale sociotechnological systems, and also the “Set-of-Strings” Framework, developed for Big Data modelling and imperfect information processing. The main constructive results of this theory, providing convergence of modern data/knowledge engineering and classic operations research, are easily software-implemented and widely useful for application to the multiple complicated problems concerning global DE. Being initially targeted to operation interval values, these tools have an evident potential for implementation in the background of quantum computing paradigm. Such research would also be initiated by the Renewed Task Group.
Major contribution to the development of the refined mathematical toolkit was done in the previous works of the TG members, including those which were published during 2019-2021:
F.S.Roberts and I.A.Sheremet (eds). Resilience in the Digital Age, LNCS 12660, Springer, Cham, 2021. P.199.
Gvishiani A., Dobrovolsky M., Rybkina A. Big Data and FAIR Data for Data Science. – In: F.S.Roberts and I.A.Sheremet (eds). Resilience in the Digital Age, LNCS 12660, 2021, pp.105-117. https://doi.org/10.1007/978-3-70370_6.
Sheremet I.A. Augmented Post Systems: The Mathematical Framework for Data and Knowledge Engineering in Network-Centric Environment. – Berlin: EANS, 2013. P.395.
Sheremet I. Data and Knowledge Bases with Incomplete Information in a “Set of Strings” Framework. – Int. J. of Eng. and Appl. Sci., Vol.3 (2016), No.3, pp.90-103.
Sheremet I.A. Augmented Post Systems: String-Operating Knowledge Representation for Big Data and Internet of Things Applications. – Geoinformatics Research Papers, Vol.5, BS 1002, doi: 10.2205/CODATA 2017, 2017.
Sheremet I. “Set of Strings” Framework for Big Data Modeling. — In: Introduction to Data Science and Machine Learning. Ed. by K.Sud, P.Erdogmus and S.Kadry. — London: IntechOpen, 2020. DOI: 10.5772/intechopen.85602 https://www.intechopen.com/books/introduction-to-data-science-and-machine-learning/-set-of-strings-framework-for-big-data-modeling.
Sheremet I. Augmented Post Systems: Syntax, Semantics, and Applications. — In: Introduction to Data Science and Machine Learning. Ed. by K.Sud, P.Erdogmus and S.Kadry. — London: IntechOpen, 2020. DOI: 10.5772/intechopen.86207 https://www.intechopen.com/online-first/augmented-post-systems-syntax-semantics-and-applications
Sheremet I.A. Recursive Multisets and their Applications. – Berlin: NG Verlag, 2011. P.249. ISBN 39429412X.
Sheremet I.A. Multiset Analysis of Consequences of Natural Disasters Impacts on Large-Scale Industrial Systems. – Data Sci. J., 17:4, pp.1-17. DOI: https://doi.org/10.5334/dsj-2018-004.
Gvishiani A.D., Roberts F.S., Sheremet I.A. On the Assessment of Sustainability of Distributed Sociotechnical Systems to Natural Disasters. – Russian Journal of Earth Sciences, Vol.18, ES4004, doi:10.225/2018ES000627, 2018.
Sheremet I. Multiset-Based Knowledge Representation for the Assessment and Optimization of Large-Scale Sociotechnical Systems. — In: Enhanced Expert Systems. Ed. by P.Vizureanu. — London: IntechOpen, 2019. DOI: 10.5772/intechopen.81698 https://www.intechopen.com/books/enhanced-expert-systems/multiset-based-knowledge-representation-for-the-assessment-and-optimization-of-large-scale-sociotech.