Top.Mail.Ru
Preview

UPRAVLENIE / MANAGEMENT (Russia)

Advanced search

Opportunities for improving the processes of preparing legal acts by public authorities based on the process mining

https://doi.org/10.26425/2309-3633-2022-10-4-96-110

Abstract

The processes of creating legal acts must meet such criteria as transparency, controllability, compliance with regulations. However, currently the procedures are extremely bureaucratic, pre-planned and go through many instances during the preparation, approval and signing. Of course, most of these processes are necessary, time-tested and legally fixed. At the same time, there are operations that require optimisation, including due to their automation or robotisation. To identify them and ensure that the procedure meet the changing needs of the state, it is important to create conditions for continuous monitoring, timely identification and operational adaptation and optimisation of the rule-making activities of the authorities. In this regard, the issue of applying contemporary technologies and approaches to analysis and the formation of recommendations for improving proactive processes seems extremely relevant. The purpose of this study is to examine the currend specifics of the preparation of the legal acts by the federal executive authorities and to identify areas for this normative documents’ improvement based on the process mining. The research methods used were a literature review and the Russian legal framework analysis, a questionnaire survey and process modelling. The authors analyse how draft legal documents (government and presidential acts, federal laws) are developed in the Russian Federation. They demonstrate the need for a transition to smart management. Its principles will ensure efficiency and flexibility in the preparation of normative legal acts. The metrics for monitoring and controlling the execution of the relevant instructions are formulated and the prospects for the development of their information support as a result of the implementation of process mining technologies are highlighted.

About the Authors

E. V. Vasilieva
Financial University under the Government of the Russian Federation
Russian Federation

Elena V. Vasilieva, Dr. Sci (Econ.), Prof. at the Business Informatics Department

49/2, Leningradsky prospekt, Moscow 125167



O. I. Dolganova
Financial University under the Government of the Russian Federation
Russian Federation

Olga I. Dolganova, Cand. Sci (Econ.), Assoc. Prof. at Financial Technologies Department

49/2, Leningradsky prospekt, Moscow 125167



References

1. Altukhova N.F., Vasilieva E.V., Deeva E.A., Dotsenko D.A., Kozlov M.A. (2020), Economics of information systems: management and performance evaluation: textbook, Knorus, Moscow, Russia (in Russian).

2. Aghabaghery R., Hashemi Golpayegani A., Esmaeili L. (2020), “A new method for organizational process model discovery through the analysis of workflows and data exchange networks”, Social Network Analysis and Mining, vol. 10, article number 12, https://doi.org/10.1007/s13278-020-0623-5

3. Batista E., Solanas A. (2019), Skip Miner: Towards the Simplification of Spaghetti-like Business Process Models, In: Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–6, https://doi.org/10.1109/IISA.2019.8900713

4. Evlanov L.G., Kutuzov V.A. (1978), Expert assessments in management, Ekonomika, Moscow, Russia (in Russian).

5. Choi D., R’bigui H. and Cho C. (2021), “Candidate digital tasks selection methodology for automation with robotic process automation”, Sustainability, vol. 13, no. 16, article number 8980, https://doi.org/10.3390/su13168980

6. Codish D., Rabin E., Ravid G. (2019), “User behavior pattern detection in unstructured processes – a learning management system case study”, Interactive Learning Environments, vol. 27, issue 5-6: The new potentials for Intelligent Tutoring with learning analytics, pp. 699–725, https://doi.org/10.1080/10494820.2019.1610456

7. Kameneva E.A. (2008), “Organization of control over the execution of orders”, Deloproizvodstvo i dokumentooborot na predpriyatii, no. 2, pp. 27–48.

8. Korotkov E.M. (2020), Research of control systems, Yurait, Moscow, Russia (in Russian).

9. Macak M., Vanát I., Merjavý M., Jevočin T., Buhnova B. (2020), “Towards Process Mining Utilization in Insider Threat Detection from Audit Logs”, In: Proceedings of the Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–6, https://doi.org/10.1109/SNAMS52053.2020.9336573

10. Myers D., Suriadi S., Radke K., Foo E. (2018), “Anomaly detection for industrial control systems using process mining”, Computers & Security, vol. 78, pp. 103–125, https://doi.org/10.1016/j.cose.2018.06.002

11. Reinkemeyer L. (ed.) (2020), Process Mining in Action, Springer, Cham, https://doi.org/10.1007/978-3-030-40172-6

12. Sangil M.J. (2020), “Heuristics-Based Process Mining on Extracted Philippine Public Procurement Event Logs”, In: Proceedings of the 7th International Conference on Behavioural and Social Computing (BESC), 2020, pp. 1–4, https://doi.org/10.1109/BESC51023.2020.9348306

13. Scholl H.J. (2020), “Digital government: looking back and ahead on a fascinating domain of research and practice”, Digital Government: Research and Practice, vol. 1, no. 1, pp. 1–12, https://doi.org/10.1145/3352682

14. Scholl H.J., Bolívar M.P.R. (2019), “Regulation as both enabler of technology use and global competitive weapon: The Gibraltar Case”, Government Information Quarterly, vol. 36, no. 3, pp. 601–613, https://doi.org/10.1016/j.giq.2019.05.003

15. Stefanini A., Aloini D., Benevento E., Dulmin R., Mininno V. (2020), “A data-driven methodology for supporting resource planning of health services”, Socio-Economic Planning Sciences, vol. 70, article number 100744, https://doi.org/10.1016/j.seps.2019.100744

16. Takei T., Horita H. (2021), “Towards Goal-Oriented Business Process Model Repair”, In: Proceedings of the 10th International Congress on Advanced Applied Informatics (IIAI-AAI), pp. 691–696, https://doi.org/10.1109/IIAI-AAI53430.2021.00123

17. Tang W., Matzner M. (2020), “Creating humanistic value with process mining for improving work conditions – A sociotechnical perspective”, In: Proceedings of the International Conference on Information Systems, ICIS 2020 – Making Digital Inclusive: Blending the Local and the Global.

18. van der Aalst W. et al. (2012), “Process Mining Manifesto”, In: Daniel F., Barkaoui K., Dustdar S. (eds) Business Process Management Workshops. BPM 2011. Lecture Notes in Business Information Processing, vol 99, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-28108-2_19

19. van der Aalst W.M.P. (2016), Process Mining: Data Science in Action, Springer, Berlin/Heidelberg, Germany.

20. Vasileva E.V., Deeva E.A. (2017), “Methods оf expert evaluations in applied information economics for the analysis оf efficiency investments in development of information systems”, The world of new economy, no. 4, pp. 14–22.


Review

For citations:


Vasilieva E.V., Dolganova O.I. Opportunities for improving the processes of preparing legal acts by public authorities based on the process mining. UPRAVLENIE / MANAGEMENT (Russia). 2022;10(4):96-110. (In Russ.) https://doi.org/10.26425/2309-3633-2022-10-4-96-110

Views: 391


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2309-3633 (Print)
ISSN 2713-1645 (Online)