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Creating of a General Purpose Language for the Construction of Dynamic Reports

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Digital Transformation (PLAIS EuroSymposium 2023)

Abstract

In digital environment, generating reports is an integral part of any business, and therefore there is a huge need for a tool for creating complex adaptive reports in investment, marketing activities, financial projects etc. The purpose of the paper is to formulate the requirements for GPL for the creation of custom reports in different business areas and build a software product that will use it. We have researched the possibilities of creating a universal language for building complex reports that is flexible enough to be used in any business domain. We have also identified the main requirements for such a language and the software product that would utilize it. We have paid particular attention to the peculiarities and problems associated with the creation and use of such a language, and have proposed ways to address them. As an experiment, we have created a prototype software module using a language based on mathematical formulas. The developed module can be used both for reports and for any calculations of companies engaged in product and business analytics.

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References

  1. Palmer, B.: What Are International Financial Reporting Standards (IFRS)? (2022). https://www.investopedia.com/terms/i/ifrs.asp/

  2. Kobets, V., Yatsenko, V., Buiak, L.: Bridging business analysts competence gaps: labor market needs versus education standards. Commun. Comput. Inf. Sci. 1308, 22–45 (2021). https://doi.org/10.1007/978-3-030-77592-6_2

    Article  Google Scholar 

  3. Kobets, V., Yatsenko, V., Mazur, A., Zubrii, M.: Data analysis of personalized investment decision making using robo-advisers. Sci. Innov. 16(2), 80–93 (2020). https://doi.org/10.15407/SCINE16.02.080

    Article  Google Scholar 

  4. Savchenko, S., Kobets, V.: Development of robo-advisor system for personalized investment and insurance portfolio generation. Commun. Comput. Inf. Sci. 1635, 213–228 (2022). https://doi.org/10.1007/978-3-031-14841-5_14

    Article  Google Scholar 

  5. Kobets, V., Petrov, O., Koval, S.: Sustainable robo-advisor bot and investment advice-taking behavior. Lect. Notes Bus. Inf. Process. 465, 15–35 (2022). https://doi.org/10.1007/978-3-031-23012-7_2

    Article  Google Scholar 

  6. Kobets, V., Tsiuriuta, N., Lytvynenko, V., Novikov, M., Chizhik, S., et al.: Recruitment web-service management system using competence-based approach for manufacturing enterprises. In: Ivanov, V., et al. (ed.) DSMIE 2019. LNME, pp. 138–148. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22365-6_14

    Chapter  Google Scholar 

  7. Kenton, W.: Business Segment Reporting Definition, Importance, Example (2021). https://www.investopedia.com/terms/b/business-segment-reporting.asp.

  8. El-khoury, J., Berezovskyi, A., Nyberg, M.: An industrial evaluation of data access techniques for the interoperability of engineering software tools. J. Ind. Inf. Integr. 15, 58–68 (2019). https://doi.org/10.1016/j.jii.2019.04.004

    Article  Google Scholar 

  9. Lu, X.: Automatic analysis of syntactic complexity in second language writing. Int. J. Corpus Linguist. 15(4), 474–496 (2010). https://doi.org/10.1075/ijcl.15.4.02lu

    Article  Google Scholar 

  10. Hayes, A.: eXtensible Business Reporting Language (XBRL): Investor's Guide (2022). https://www.investopedia.com/terms/x/xbrl.asp.

  11. Bondar, S., Ruppert, C., Stjepandić, J.: Ensuring data quality beyond change management in virtual enterprise. Int. J. Agile Syst. Manag. 7(3–4), 304–323 (2014). https://doi.org/10.1504/IJASM.2014.065346

    Article  Google Scholar 

  12. Nguyen, M.-T., Le, D.T., Le, L.: Transformers-based information extraction with limited data for domain-specific business documents. Eng. Appl. Artif. Intell. 97, 104100 (2021)

    Article  Google Scholar 

  13. Seng, J.-L., Lai, J.T.: An Intelligent information segmentation approach to extract financial data for business valuation. Expert Syst. Appl. 37, 6515–6530 (2010). https://doi.org/10.1016/j.eswa.2010.02.134

    Article  Google Scholar 

  14. Duque, J., Godinhob, A., Vasconceloscd, J.: Knowledge data extraction for business intelligence. Procedia Comput. Sci. 204, 131–139 (2022)

    Article  Google Scholar 

  15. Giner-Miguelez, J., Gómez, A., Cabot, J.: A domain-specific language for describing machine learning datasets. J. Comput. Lang. 76, 101209 (2023)

    Article  Google Scholar 

  16. Quintero, A.M.R., Pérez, S.M., Varela-Vaca, A.J., López, M.T.G., Cabot, J.: A domain-specific language for the specification of UCON policies. J. Inf. Secur. Appl. 64, 103006 (2022)

    Google Scholar 

  17. Vidal, M., Massoni, T., Ramalho, F.: A domain-specific language for verifying software requirement constraints. Sci. Comput. Program. 197, 102509 (2020)

    Article  Google Scholar 

  18. Chavarriaga, E., Jurado, F., Rodríguez, F.D.: An approach to build JSON-based domain specific languages solutions for web applications. J. Comput. Lang. 75, 101203 (2023)

    Article  Google Scholar 

  19. Rodrígueza, A., Macíasd, F., Duránc, F., Rutle, A., Wolter, U.: Composition of multilevel domain-specific modelling languages. J. Logical Algebr. Methods Program. 130, 100831 (2023)

    Article  MathSciNet  Google Scholar 

  20. Aysolmaz, B., Leopold, H., Reijers, H.A., Demirörs, O.: A semi-automated approach for generating natural language requirements documents based on business process models. Inf. Softw. Technol. 93, 14–29 (2018). https://doi.org/10.1016/j.infsof.2017.08.009

    Article  Google Scholar 

  21. Enia, L.C.: Empirical research: exploring extensible business reporting language and views of Romanian accountants. Procedia Econ. Finan. 32, 1675–1699 (2015). https://doi.org/10.1016/S2212-5671(15)01495-1

    Article  Google Scholar 

  22. Behera, R.K., Bala, P.K., Rana, N.P., Irani, Z.: Responsible natural language processing: a principlist framework for social benefits. Technol. Forecast. Soc. Chang. 188, 122306 (2023). https://doi.org/10.1016/j.techfore.2022.122306

    Article  Google Scholar 

  23. Choia, J., Jeong, B., Yoonc, J.: Identification of emerging business areas for business opportunity analysis: an approach based on language model and local outlier factor. Comput. Ind. 140, 103677 (2022). https://doi.org/10.1016/j.compind.2022.103677

    Article  Google Scholar 

  24. Kobeissi, M., Assy, N., Gaaloul, W., Defude, B., Benatallah, B., Haidar, B.: Natural language querying of process execution data. Inf. Syst. 116, 102227 (2023). https://doi.org/10.1016/j.is.2023.102227

    Article  Google Scholar 

  25. Best, R.: Best Asset Management Software (2023). https://www.investopedia.com/best-asset-management-software-5090064

  26. Carmody, B.: Best Tenant Screening Services (2023). https://www.investopedia.com/best-tenant-screening-services-5070361

  27. Kenton, W.: Visual Basic for Applications (VBA): Definition, Uses, Examples (2022). https://www.investopedia.com/terms/v/visual-basic-for-applications-vba.asp.

  28. Hicks, M., Levin, D.: CMSC 330: Organization of Programming Languages (2013). https://www.coursehero.com/file/178765173/org-of-Progpdf/

  29. ANother Tool for Language Recognition. https://www.antlr.org/documentation.html. Accessed 29 May 2023

  30. ANTLR. https://github.com/antlr/antlr4. Accessed 29 May 2023

  31. JAVACC, https://javacc.github.io/javacc/documentation/. Accessed 29 May 2023

  32. GNU Bison. https://www.gnu.org/software/bison/. Accessed 29 May 2023

  33. Hibernate. https://hibernate.org/. Accessed 29 May 2023

  34. Spring Data. https://spring.io/projects/spring-data. Accessed 29 May 2023

  35. Apache POI. https://poi.apache.org/. Accessed 29 May 2023

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Iatsiuta, V., Kobets, V., Ivanov, O. (2023). Creating of a General Purpose Language for the Construction of Dynamic Reports. In: Maślankowski, J., Marcinkowski, B., Rupino da Cunha, P. (eds) Digital Transformation. PLAIS EuroSymposium 2023. Lecture Notes in Business Information Processing, vol 495. Springer, Cham. https://doi.org/10.1007/978-3-031-43590-4_2

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  • DOI: https://doi.org/10.1007/978-3-031-43590-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43589-8

  • Online ISBN: 978-3-031-43590-4

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