Abstract
This paper discusses the specific kind of uncertainties, which appear in ontology-driven software development. We focus on the development of IoT applications whose source code is generated automatically by an ontology-driven framework. So-called “compatibility uncertainties” pop up when the ontology is being changed while the corresponding generated application is in operation. This specific kind of uncertainties can be treated as a variant of implementation uncertainties. The algorithm of its automated handling is presented. The proposed algorithm is implemented within the SciVi platform and tested in the real-world project devoted to the development of custom IoT-based hardware user interfaces for virtual reality. We use the SciVi platform as a toolset for the automatic generation of IoT devices firmware for ontology-driven Edge Computing but the problem discussed is common for any tools which are used for the generation of ontology-driven software.
This study is supported by the research grant No. ID92566385 from Saint Petersburg State University.
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L1 and L2 stand for the native and foreign languages respectively.
References
Abdulrab, H., Babkin, E., Kozyrev, O.: Semantically enriched integration framework for ubiquitous computing environment. In: Babkin, E. (ed.) Ubiquitous Computing, chap. 9, pp. 177–196. IntechOpen (2011). https://doi.org/10.5772/15262
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)
Calderon, M., Delgadillo, S., Garcia-Macias, A.: A more human-centric internet of things with temporal and spatial context. Procedia Comput. Sci. 83, 553–559 (2016). https://doi.org/10.1016/j.procs.2016.04.263
Chuprina, S., Ryabinin, K., Koznov, D., Matkin, K.: Ontology-driven visual analytics software development. Program. Comput. Softw. 48(3), 208–214 (2022). https://doi.org/10.1134/S0361768822030033
Dibowski, H., Kabitzsch, K.: Ontology-based device descriptions and device repository for building automation devices. EURASIP J. Embed. Syst. 2011(1), 1–17 (2011). https://doi.org/10.1155/2011/623461
Golitsyna, O.L., Maksimov, N.V., Okropishina, O.V., Strogonov, V.I.: The ontological approach to the identification of information in tasks of document retrieval. Autom. Documentation Math. Linguist. 46, 125–132 (2012). https://doi.org/10.3103/S0005105512030028
Hansen, C., Hansen, C., Simonsen, J.G., Alstrup, S., Lioma, C.: Unsupervised multi-index semantic hashing. In: Proceedings of the Web Conference 2021, pp. 2879–2889 (2021). https://doi.org/10.1145/3442381.3450014
Hernández-Illera, A., Martínez-Prieto, M.A., Fernández, J.D.: RDF-TR: exploiting structural redundancies to boost RDF compression. Inf. Sci. 508, 234–259 (2020). https://doi.org/10.1016/j.ins.2019.08.081
Jara, A.J., Olivieri, A.C., Bocchi, Y., Jung, M., Kastner, W., Skarmeta, A.F.: Semantic web of things: an analysis of the application semantics for the IoT moving towards the IoT convergence. Int. J. Web Grid Serv. 10(2/3), 244–272 (2014). https://doi.org/10.1504/IJWGS.2014.060260
Kernighan, B.W., Ritchie, D.M.: C Programming Language. Prentice-Hall (1988)
Mao, S., et al.: Chapter 14 - Ubiquitous Computing. In: Godfrey, A., Stuart, S. (eds.) Digital Health, pp. 211–230. Academic Press (2021). https://doi.org/10.1016/B978-0-12-818914-6.00002-8
Martínez-Prieto, M.A., Fernández, J.D., Hernández-Illera, A., Gutiérrez, C.: RDF compression. In: Sakr, S., Zomaya, A. (eds.) Encyclopedia of Big Data Technologies, pp. 1–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-63962-8_62-1
Pan, J.Z., Staab, S., Aßmann, U., Ebert, J., Zhao, Y. (eds.): Ontology-Driven Software Development. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-31226-7
Patel, A., Debnath, N.C., Bhushan, B. (eds.): Semantic Web Technologies: Research and Applications, 1st edn. CRC Press (2022). https://doi.org/10.1201/9781003309420
Pearson, P.K.: Fast hashing of variable-length text strings. Commun. ACM 33(6), 677–680 (1990). https://doi.org/10.1145/78973.78978
Pinho, D., Aguiar, A., Amaral, V.: What about the usability in low-code platforms? A systematic literature review. J. Comput. Lang. 74 (2023). https://doi.org/10.1016/j.cola.2022.101185
Qaswar, F., et al.: Applications of ontology in the internet of things: a systematic analysis. Electronics 12(1) (2023). https://doi.org/10.3390/electronics12010111
Rhayem, A., Mhiri, M.B.A., Gargouri, F.: Semantic web technologies for the internet of things: systematic literature review. Internet Things 11 (2020). https://doi.org/10.1016/j.iot.2020.100206
Rivest, R.: The MD5 Message-Digest Algorithm. RFC 1321, RFC Editor (1992). https://doi.org/10.17487/RFC1321
Röder, M., Frerk, P., Conrads, F., Ngomo, A.-C.N.: Applying grammar-based compression to RDF. In: Verborgh, R., et al. (eds.) ESWC 2021. LNCS, vol. 12731, pp. 93–108. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77385-4_6
Roza, M.: Verification, Validation and Uncertainty Quantification Methods and Techniques (An Overview and their Application within the GM-VV Technical Framework). Science and Technology Organization, NATO (2014)
Ruta, M., Scioscia, F., Di Sciascio, E.: Enabling the semantic web of things: framework and architecture. In: 2012 IEEE Sixth International Conference on Semantic Computing, pp. 345–347 (2012). https://doi.org/10.1109/ICSC.2012.42
Ryabinin, K., Belousov, K.: Visual analytics of gaze tracks in virtual reality environment. Sci. Vis. 13(2), 50–66 (2021). https://doi.org/10.26583/sv.13.2.04
Ryabinin, K., Chumakov, R., Belousov, K., Kolesnik, M.: Ontology-driven visual analytics platform for semantic data mining and fuzzy classification. Frontiers Artif. Intell. Appl. 358, 1–7 (2022). https://doi.org/10.3233/FAIA220363
Ryabinin, K., Chuprina, S.: Ontology-driven edge computing. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12143, pp. 312–325. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50436-6_23
Ryabinin, K., Chuprina, S., Labutin, I.: Tackling IoT interoperability problems with ontology-driven smart approach. In: Rocha, A., Isaeva, E. (eds.) Perm Forum 2021. LNNS, vol. 342, pp. 77–91. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-89477-1_9
Sahlmann, K., Scheffler, T., Schnor, B.: Ontology-driven device descriptions for IoT network management. In: 2018 Global Internet of Things Summit (GIoTS) (2018). https://doi.org/10.1109/GIOTS.2018.8534569
Sahlmann, K., Schwotzer, T.: Ontology-based virtual IoT devices for edge computing. In: Proceedings of the 8th International Conference on the Internet of Things (2018). https://doi.org/10.1145/3277593.3277597
Scioscia, F., Ruta, M.: Building a semantic web of things: issues and perspectives in information compression. In: 2009 IEEE International Conference on Semantic Computing, pp. 589–594 (2009). https://doi.org/10.1109/ICSC.2009.75
Seitz, C., Schönfelder, R.: Rule-based OWL reasoning for specific embedded devices. In: Aroyo, L., et al. (eds.) ISWC 2011. LNCS, vol. 7032, pp. 237–252. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25093-4_16
Slimani, T.: Ontology development: a comparing study on tools, languages and formalisms. Indian J. Sci. Technol. 8(24), 1–12 (2015). https://doi.org/10.17485/ijst/2015/v8i34/54249
Su, X., Riekki, J., Haverinen, J.: Entity notation: enabling knowledge representations for resource-constrained sensors. Pers. Ubiquit. Comput. 16, 819–834 (2012). https://doi.org/10.1007/s00779-011-0453-6
Sultana, T., Lee, Y.K.: gRDF: an efficient compressor with reduced structural regularities that utilizes gRePair. Sensors 22(7) (2022). https://doi.org/10.3390/s22072545
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Ryabinin, K., Chuprina, S. (2023). Semantic Hashing to Remedy Uncertainties in Ontology-Driven Edge Computing. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 10477. Springer, Cham. https://doi.org/10.1007/978-3-031-36030-5_52
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