List of issues > Series «Mathematics». 2017. Vol. 22
Locally Simple Models Construction: Methodology and Practice
One of the most notable trends associated with the Fourth industrial revolution is a significant strengthening of the role played by semantic methods. They are engaged in artificial intelligence means, knowledge mining in huge flows of big data, robotization, and in the internet of things. Smart contracts also can be mentioned here, although the ’intelligence’ of smart contracts still needs to be seriously elaborated. These trends should inevitably lead to an increased role of logical methods working with semantics, and significantly expand the scope of their application in practice. However, there are a number of problems that hinder this process.
We are developing an approach, which makes the application of logical modeling efficient in some important areas. The approach is based on the concept of locally simple models and is primarily focused on solving tasks in the management of enterprises, organizations, governing bodies. The most important feature of locally simple models is their ability to replace software systems. Replacement of programming by modeling gives huge advantages, for instance, it dramatically reduces development and support costs. Modeling, unlike programming, preserves the explicit semantics of models allowing integration with artificial intelligence and robots. In addition, models are much more understandable to general people than programs.
In this paper we propose the implementation of the concept of locally simple modeling on the basis of so-called document models, which has been developed by us earlier. It is shown that locally simple modeling is realized through document models with finite submodel coverages. In the second part of the paper an example of using document models for solving a management problem of real complexity is demonstrated.
For citation:
Kazakov I.A., Kustova I.A., Lazebnikova E.N., Mantsivoda A.V. Locally Simple Models Construction: Methodology and Practice. The Bulletin of Irkutsk State University. Series Mathematics, 2017, vol. 22, pp. 71-89. (In Russian). https://doi.org/10.26516/1997-7670.2017.22.71
1. Vityev Е. Semantic Probablistic Inference of Predictions. Izv. Irkutsk. Gos. Univ. Ser. Mat., 2017, vol. 21. (in Russian). https://doi.org/10.26516/1997-7670.2017.21.33
2. Malykh A., Mantsivoda A. Document modeling. Izv. Irkutsk. Gos. Univ. Ser. Mat., 2017, vol. 21, p. 33-55 (in Russian). https://doi.org/10.26516/1997-7670.2017.21.89
3. Malykh A., Mantsivoda A. Object Theories over List Superstructures. Izv. Irkutsk. Gos. Univ. Ser. Mat., 2012, vol. 4, pp. 27-44. (in Russian)
4. Malykh A., Mantsivoda A. Sistema Libretto: razrabotka web-resursov v edinoi modeli dannykh i znanii [Libretto System: Web Resources Development Based On a Holistic Data and Knowledge Model]. Proceedings of The 6th All-RussianConference on Control Problems. Gelendzhik, September, 2013, pp. 73-75.
5. Malykh A., Mantsivoda A. Ontobox: Smart Contracts as Document models. URL: https://ontobox.io/ppe.
6. Schwab K. The Fourth Industrial Revolution. World Economic Forum, 2015. 120 p.
7. Programming Language Libretto. - URL: http://librettolabs.com.
8. Antonopoulos A., Wood G. Mastering Ethereum: Building Smart Contracts and Dapps. O’Reilly Media, 2017. 220 p.
9. Baader F., Calvanese D., McGuinness D.L., Nardi D., Patel-Schneider P.F. (eds.) The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, 2003.
10. Berners-Lee T., Hendler J., Lassila O. The Semantic Web Scientific American. May, 2001. https://doi.org/10.1038/scientificamerican0501-34
11. Semantic Web activity. URL: https://www.w3.org/standards/semanticweb/
12. Goncharov S.S. Conditional terms in semantic programming. Siberian Mathematical Journal, vol. 58, no 5, pp. 794-800. https://doi.org/10.1134/S0037446617050068
13. Goncharov S.S., Ershov Yu.L., Sviridenko D.I. Semantic foundations of programming. Lecture Notes in Computer Science. 1987, vol. 278, pp. 116-122. https://doi.org/10.1007/3-540-18740-5_28
14. Goncharov S.S., Ershov Yu.L., Sviridenko D.I. Semantic programming. Information processing, Proc. IFIP 10-th World Comput. Congress. Dublin, 1986, vol. 10, pp. 1093-1100.
15. Horrocks I., Patel-Schneider P., Van Harmelen F. From SHIQ and RDF to OWL: The making of a Web Ontology Language. Journal of Web Semantics, 2003, vol. 1, Issue 1, pp. 7-26. https://doi.org/10.1016/j.websem.2003.07.001
16. Horrocks I., Sattler U., Tobies U. Practical reasoning for expressive description logics. In: H. Ganzinger, D. McAllester and A. Voronkov (eds.). Proceedings of the 6th International Conference on Logic for Programming and Automated Reasoning (LPAR’99), number 1705 in Lecture Notes in Artificial Intelligence, Springer-Verlag, 1999, pp. 161-180. https://doi.org/10.1007/3-540-48242-3_11
17. Kovalerchuk B., Vityaev E. Data Mining in finance: Advances in Relational and Hybrid Methods. Kluwer Academic Publishers, 2000. 456 p.
18. Malykh A., Mantsivoda A. A Query Language for Logic Architectures. Lecture Notes in Computer Science 5947. Springer-Verlag, Berlin Heidelberg, 2010, pp. 294–305. https://doi.org/10.1007/978-3-642-11486-1_25
19. Schumpeter J. Not-so-clever Contracts. The Economist. The new political divide. Issue July 31, 2016. URL: https://www.economist.com/news/business/21702758-time-being-least-human-judgment-still-better-bet-cold-hearted.
20. Swan M. Blockchain: Blueprint for a New Economy. O’Reilly Media, 2015. 127 p.
21. Wattenhofer R. The Science of the Blockchain. Inverted Forest Publishing, 2016. 320 p.