Skip to main content

How Cognitive Processes Make Us Smarter

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 362))

Abstract

Cognitive computing describes the learning effects that computer systems can achieve through training and via interaction with human beings. Developing capabilities like this requires large datasets, user interfaces with cognitive functions, as well as interfaces to other systems so that information can be exchanged and meaningfully linked. Recently, cognitive computing has been applied within business process management (BPM), raising the question about how cognitive computing may change BPM, and even leverage some new cognitive resources. We believe that the answer to this question is linked to the promised learning effects for which we need to explore how cognitive processes enable learning effects in BPM. To this end, we collect and analyze publications on cognitive BPM from research and practice. Based on this information, we describe the principle of cognitive process automation and discuss its practical implications with a focus on technical synergies. The results are used to build a visual research map for cognitive BPM.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. van der Aa, H., et al.: Challenges and opportunities of applying natural language processing in business process management. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 2791–2801 (2018)

    Google Scholar 

  2. van der Aa, H., Di Ciccio, C., Leopold, H., Reijers, H.A.: Extracting declarative process models from natural language. In: Giorgini, P., Weber, B. (eds.) CAiSE 2019. LNCS, vol. 11483, pp. 365–382. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21290-2_23

    Chapter  Google Scholar 

  3. van der Aalst, W.M.P., et al.: Robotic process automation. Bus. Inf. Syst. Eng. 60(4), 269–272 (2018)

    Article  Google Scholar 

  4. Aguirre, S., Rodriguez, A.: Automation of a business process using robotic process automation (RPA): a case study. In: Figueroa-García, J.C., López-Santana, E.R., Villa-Ramírez, J.L., Ferro-Escobar, R. (eds.) WEA 2017. CCIS, vol. 742, pp. 65–71. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66963-2_7

    Chapter  Google Scholar 

  5. Arora, C., et al.: Automated checking of conformance to requirements templates using natural language processing. IEEE Trans. Softw. Eng. 41(10), 944–968 (2015)

    Article  Google Scholar 

  6. Asatiani, A., Penttinen, E.: Turning robotic process automation into commercial success – case OpusCapita. J. Inf. Technol. Teach. Cases. 6(2), 67–74 (2016)

    Article  Google Scholar 

  7. Aysolmaz, B., et al.: A semi-automated approach for generating natural language requirements documents based on business process models. Inf. Softw. Technol. 93, 14–29 (2018)

    Article  Google Scholar 

  8. Baesens, B., et al.: Challenges of smart business process management: an introduction to the special issue. Decis. Support Syst. 100, 1–5 (2017)

    Article  Google Scholar 

  9. Betz, S., Klink, S.: Automatic user support for business process modeling. In: Proceedings of the Workshop on Semantics for Business Process Management, pp. 1–12 (2006)

    Google Scholar 

  10. Caracaş, A., Bernauer, A.: Compiling business process models for sensor networks. In: Proceedings of the International Conference on Distributed Computing in Sensor Systems and Workshops (2011)

    Google Scholar 

  11. Chen, M., et al.: Cognitive computing: architecture, technologies and intelligent applications. IEEE Access 6, 19774–19783 (2018)

    Article  Google Scholar 

  12. Cheng, Y., et al.: Modeling and deploying IoT-aware business process applications in sensor networks. Sensors 19, 1 (2018)

    Article  Google Scholar 

  13. Davenport, T.H., Kirby, J.: Just how smart are smart machines? MIT Sloan Manag. Rev. 57, 3 (2016)

    Google Scholar 

  14. Dwarkanhalli, H., et al.: How Cognitive Computing Unlocks Business Process Management’s Performance – Enhancing Virtues (2018)

    Google Scholar 

  15. Evermann, J., et al.: Predicting process behaviour using deep learning. Decis. Support Syst. 100, 129–140 (2017)

    Article  Google Scholar 

  16. Ferreira, R.C.B., et al.: A semi-automatic approach to identify business process elements in natural language texts. In: Proceedings of the International Conference on Enterprise Systems, pp. 250–261 (2017)

    Google Scholar 

  17. Figl, K., Laue, R.: Cognitive complexity in business process modeling. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 452–466. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21640-4_34

    Chapter  Google Scholar 

  18. Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Carmona, J., Engels, G., Kumar, A. (eds.) BPM 2017. LNCS, vol. 10445, pp. 252–268. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65000-5_15

    Chapter  Google Scholar 

  19. Google Scholar: Content Coverage. https://scholar.google.de/intl/de/scholar/help.html#coverage

  20. James, H., Daugherty, P.R.: Collaborative intelligence: humans and AI are joining forces. Harv. Bus. Rev. 96, 114–123 (2018)

    Google Scholar 

  21. Holzinger, A., et al.: Machine learning and knowledge extraction in digital pathology needs an integrative approach. In: Holzinger, A., Goebel, R., Ferri, M., Palade, V. (eds.) Towards Integrative Machine Learning and Knowledge Extraction. LNCS (LNAI), vol. 10344, pp. 13–50. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69775-8_2

    Chapter  Google Scholar 

  22. Houy, C., et al.: On the theoretical foundations of research into the understandability of business process models. In: Proceedings of the European Conference on Information Systems, pp. 1–38 (2014)

    Google Scholar 

  23. Hull, R., Motahari Nezhad, H.R.: Rethinking BPM in a cognitive world: transforming how we learn and perform business processes. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 3–19. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45348-4_1

    Chapter  Google Scholar 

  24. IBM: Cognitive Business Operations: Processes and decisions that sense, respond, and learn (2016)

    Google Scholar 

  25. Jarrahi, M.H.: Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making. Bus. Horiz. 61(4), 577–586 (2018)

    Article  Google Scholar 

  26. Jones, O., Gatrell, C.: Editorial: The future of writing and reviewing for IJMR (2014). http://doi.wiley.com/10.1111/ijmr.12038

  27. Kammerer, K., et al.: Towards context-aware process guidance in cyber-physical systems with augmented reality. In: Proceedings of the 4th International Workshop on Requirements Engineering for Self-Adaptive, Collaborative, and Cyber Physical Systems, pp. 44–51 (2018)

    Google Scholar 

  28. Kirschner, P.A.: Cognitive load theory: implications of cognitive load theory on the design of learning. Learn. Instr. 12(1), 1–10 (2002)

    Article  Google Scholar 

  29. Kluza, K., et al.: Overview of recommendation techniques in business process modeling? In: CEUR Workshop Proceedings, pp. 46–57 (2013)

    Google Scholar 

  30. KPMG: Embracing the cognitive era. Using automation to break transformation barriers – and make every employee an innovator (2016)

    Google Scholar 

  31. Lu, H., et al.: Brain intelligence: go beyond artificial intelligence. Mob. Netw. Appl. 23(2), 368–375 (2018)

    Article  Google Scholar 

  32. Marrella, A., Mecella, M.: Cognitive business process management for adaptive cyber-physical processes. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 429–439. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_33

    Chapter  Google Scholar 

  33. Mendling, J., et al.: How do machine learning, robotic process automation, and blockchains affect the human factor in business process management? Commun. Assoc. Inf. Syst. 43(1), 297–320 (2018)

    Google Scholar 

  34. Metzger, A., Föcker, F.: Predictive business process monitoring considering reliability estimates. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 445–460. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59536-8_28

    Chapter  Google Scholar 

  35. Najafabadi, M.M., et al.: Deep learning applications and challenges in big data analytics. J. Big Data. 2, 1 (2015)

    Article  Google Scholar 

  36. Noor, A.K.: Potential of cognitive computing and cognitive systems. Open Eng. 5(1), 75–88 (2015)

    Google Scholar 

  37. Paschek, D., et al.: Automated business process management–in times of digital transformation using machine learning or artificial intelligence. In: MATEC Web of Conferences (2017)

    Google Scholar 

  38. Pryss, R., et al.: Integrating Mobile Tasks with Business Processes: A Self-Healing Approach (2014)

    Google Scholar 

  39. Razavian, M., Turetken, O., Vanderfeesten, I.: When cognitive biases lead to business process management issues. In: Dumas, M., Fantinato, M. (eds.) BPM 2016. LNBIP, vol. 281, pp. 147–156. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58457-7_11

    Chapter  Google Scholar 

  40. Reynolds, H.: Big Data and Cognitive Computing. cognitivecomputingconsortium.com

  41. Roeglinger, M., Seyfried, J., Stelzl, S., Muehlen, M.: Cognitive computing: what’s in for business process management? an exploration of use case ideas. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 419–428. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_32

    Chapter  Google Scholar 

  42. Samek, W., et al.: Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models (2017)

    Google Scholar 

  43. Schneider, S., Leyer, M.: Me or information technology? Adoption of artificial intelligence in the delegation of personal strategic decisions. Manag. Decis. Econ. 40(3), 223–231 (2019)

    Article  Google Scholar 

  44. Seth, N., et al.: A conceptual model for quality of service in the supply chain. Int. J. Phys. Distrib. Logist. Manag. 36(7), 547–575 (2006)

    Article  Google Scholar 

  45. Siemens, G.: Learning analytics: the emergence of a discipline. Am. Behav. Sci. 57(10), 1380–1400 (2013)

    Article  Google Scholar 

  46. Smith, H., Fingar, P.: Business Process Management: The Third Wave. Meghan-Kiffer Press, Tampa (2003)

    Google Scholar 

  47. Sunkle, S., et al.: Informed active learning to aid domain experts in modeling compliance. In: Proceedings of the IEEE 20th International Enterprise Distributed Object Computing Conference, pp. 129–138 (2016)

    Google Scholar 

  48. Tarafdar, M., et al.: Enterprise cognitive computing applications: opportunities and challenges. IT Prof. 19(4), 21–27 (2017)

    Article  Google Scholar 

  49. Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 477–492. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59536-8_30

    Chapter  Google Scholar 

  50. Tranquillini, S., et al.: Process-based design and integration of wireless sensor network applications. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 134–149. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32885-5_10

    Chapter  Google Scholar 

  51. Wang, J., et al.: Deep learning for sensor-based activity recognition: a survey. Pattern Recognit. Lett. 119, 3–11 (2019)

    Article  Google Scholar 

  52. Wang, M., Wang, H.: From process logic to business logic - a cognitive approach to business process management. Inf. Manag. 43(2), 179–193 (2006)

    Article  Google Scholar 

  53. Wang, Y., et al.: Cognitive informatics and cognitive computing in year 10 and beyond. Int. J. Cogn. Informatics Nat. Intell. 5(4), 1–21 (2012)

    MathSciNet  Google Scholar 

  54. Willcocks, L., et al.: robotic process automation at Xchanging. In: The Outsourcing Unit Working Research Paper Series, pp. 1–26 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrea Zasada .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zasada, A. (2019). How Cognitive Processes Make Us Smarter. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds) Business Process Management Workshops. BPM 2019. Lecture Notes in Business Information Processing, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-030-37453-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-37453-2_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37452-5

  • Online ISBN: 978-3-030-37453-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics