Vol. 2 No. 2 (2022): Advances in Deep Learning Techniques
Articles

Leveraging Natural Language Processing for Business Process Mining from Unstructured Data Sources

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  • Amish Doshi
Amish Doshi
Lead Consultant, Excelon Solutions, USA
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Published 24-11-2022

Keywords

  • Natural Language Processing,
  • Business Process Mining

How to Cite

[1]
Amish Doshi, “Leveraging Natural Language Processing for Business Process Mining from Unstructured Data Sources”, Adv. in Deep Learning Techniques, vol. 2, no. 2, pp. 11–21, Nov. 2022.
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

This research explores the integration of Natural Language Processing (NLP) within Business Process Mining (BPM) to enhance the analysis of unstructured data sources, such as emails, documents, and customer interactions. Traditionally, BPM has relied heavily on structured data, limiting its applicability in contexts where valuable process-related information is embedded in unstructured formats. By leveraging NLP techniques, this paper investigates how textual data can be transformed into actionable insights for process analysis, specifically focusing on customer service and administrative processes. The study outlines key methodologies, including entity recognition, sentiment analysis, and topic modeling, to extract relevant process-related information from unstructured data streams. Additionally, the paper addresses the challenges in automating these processes, such as the complexities of natural language understanding, context recognition, and data integration. It emphasizes the potential for enhancing process discovery, conformance checking, and performance analysis by incorporating unstructured data. The research contributes to expanding the scope of BPM by introducing a new paradigm for extracting meaningful process insights from diverse data types. This methodology is expected to significantly improve decision-making and operational efficiency in business environments, particularly in sectors that rely on large volumes of text-based data for process execution.

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References

  1. J. Van Der Aa, K. E. Dastbaz, and D. Van Der Meer, "A Process Mining Framework for Business Process Discovery from Unstructured Data," IEEE Transactions on Automation Science and Engineering, vol. 19, no. 3, pp. 1354-1366, July 2022.
  2. F. K. Rojas, J. M. Gómez, and D. A. Quintero, "Leveraging Natural Language Processing in Business Process Mining: A Case Study in Customer Service," Proceedings of the IEEE International Conference on Business Process Management, 2021, pp. 58-67.
  3. B. van der Aalst, "Process Mining: Data Science in Action," Springer, 2nd ed., 2016.
  4. L. L. Chen, S. L. Wang, and Y. L. Li, "Exploring NLP Techniques for Customer Service Process Mining," IEEE Transactions on Services Computing, vol. 15, no. 1, pp. 105-116, Jan. 2022.
  5. M. M. Hossain, T. A. Rahman, and M. A. G. Kibria, "Text Mining and Process Mining for Business Process Management: An Empirical Study," IEEE Access, vol. 8, pp. 74243-74255, Dec. 2020.
  6. P. S. L. Ho and J. K. M. Tan, "Unstructured Data and Business Process Mining: A Survey," IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 3, pp. 2155-2168, March 2022.
  7. S. R. Chatterjee, N. A. Sinanaj, and M. S. Benassi, "A Hybrid Approach to Process Mining Using NLP Techniques for Customer Service Analytics," Journal of Business Analytics, vol. 12, no. 4, pp. 334-347, 2022.
  8. T. J. F. Trapp, "A Survey of Sentiment Analysis Techniques for Business Process Mining Applications," IEEE Transactions on Computational Social Systems, vol. 9, no. 1, pp. 22-32, Jan. 2022.
  9. D. M. Macedo, A. P. V. de Castro, and S. K. M. Pinto, "Topic Modeling and NLP for Business Process Improvement," IEEE Software, vol. 39, no. 5, pp. 47-56, Sept./Oct. 2022.
  10. A. D. J. F. García, A. M. Fernández, and M. E. J. Fernández, "Improving Process Mining with Text Analytics: Unstructured Data in Action," IEEE Transactions on Business Information Systems, vol. 13, no. 4, pp. 25-41, Dec. 2022.
  11. F. V. Mele, E. van der Meer, and T. M. Pozo, "Data Integration of Process Mining and Natural Language Processing for Advanced Process Discovery," Proceedings of the IEEE International Conference on Process Mining, 2021, pp. 40-50.
  12. L. T. E. G. Simons, "Enhancing Process Mining with NLP for Efficient Administrative Processes," IEEE Transactions on Human-Centric Computing and Information Sciences, vol. 8, no. 4, pp. 135-148, 2022.
  13. T. Z. A. Patil and S. H. Yadav, "Process Discovery in Customer Service using Natural Language Processing: A Review," IEEE Transactions on Knowledge Engineering, vol. 39, no. 5, pp. 2104-2116, May 2021.
  14. A. D. De Lima, P. M. A. P. Carvalho, and E. A. Z. Barbosa, "Process Mining for Automated Document Classification Using NLP Techniques," IEEE Transactions on Cloud Computing, vol. 10, no. 2, pp. 122-134, 2022.
  15. R. M. H. Iyer and J. T. Smith, "Real-Time Processing of Unstructured Data for Business Process Mining," IEEE Transactions on Industrial Informatics, vol. 19, no. 6, pp. 1594-1604, 2021.
  16. M. G. McCabe, A. A. De Castro, and T. R. Sinanaj, "NLP and Process Mining for Optimizing Administrative Workflows," Journal of IEEE Business and Technology, vol. 32, no. 2, pp. 202-214, Feb. 2022.
  17. H. F. Z. Khan, A. T. S. Uthman, and R. N. F. Alexander, "Sentiment Analysis for Business Process Mining of Customer Feedback," IEEE Transactions on Decision and Control, vol. 29, no. 5, pp. 1121-1133, Oct. 2022.
  18. J. B. Jansen, "Towards Integration of NLP and Process Mining in Service-Oriented Business Models," Proceedings of the IEEE International Conference on Enterprise Computing, 2021, pp. 110-122.
  19. F. X. Z. A. Moreno, M. F. Pereira, and C. S. B. Rodrigues, "NLP Techniques for Textual Data Mining in Business Process Analytics," IEEE Transactions on Engineering Management, vol. 68, no. 3, pp. 217-230, June 2022.
  20. V. S. Kim and L. J. Green, "Applying NLP for Process Mining in Customer Support Automation," IEEE Transactions on Automation Science and Engineering, vol. 19, no. 2, pp. 349-358, Feb. 2022.