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technical level of process specification. Context, which is the third element taken
into account, is addressed at the same technical level. Tying these three elements
together, this work presents a systematic process for learning and achieving con-
stant improvement. Addressing both hard and soft goals, our approach is expected
to reduce the frequency of exceptional terminations of the process and to improve
business performance over time.
The learning process we propose draws conclusions from experience gained over
time while executing a business process. Comparing this kind of learning to human
learning, a major difference is that humans are capable of learning from mistakes
they make, by acknowledging a certain decision as a wrong decision that should
not be repeated. Humans can avoid repeating a mistake that has only been made
once. In contrast, our learning process is statistical in nature; hence it can only draw
conclusions after a substantial number of repetitions have been made. To improve
the ability of the approach to learn from episodic failures (or successes), other kinds
of reasoning mechanisms (e.g., Case-based reasoning) can be used in combination
with the one proposed here. Future research will develop a set of learning mech-
anisms that can be used in combination, so each is applied in different situations.
Future research will also experiment with the learning application and validate it in
real life processes.
References
1. van der Aalst WMP, van Dongen BF (2002) Discovering workflow performance models
from timed logs. In: Han Y et al (eds) Proceedings of the international conference on engi-
neering and deployment of cooperative information systems. LNCS, vol 2480. Springer,
Berlin/Heidelberg
2. van der Aalst WMP (2005) Business alignment: using process mining as a tool for delta
analysis and conformance testing. Reqs Eng 10(3):198-211
3. Adams M, ter Hofstede AHM, van der Aalst WMP, Edmond D (2007) Dynamic, extensible
and context-aware exception handling for workflows. In: Proceedings of OTM 2007 Part 1.
LNCS, vol 4803. Springer, Berlin/Heidelberg
4. Andersson B, Bider I, Johannesson P, Perjons E (2005) Towards a formal definition of goal-
oriented business process patterns. Business Process Manage J 11(6):650-662
5. Davenport TH (1993) Process innovation, reengineering work through information technol-
ogy. Harvard Business School Press, Boston, MA
6. van Dongen BF, Adriansyah A (2009) Process mining: fuzzy clustering and performance visu-
alization. In: Proceedings of the 5th international workshop on business process intelligence
(BPI 2009), Ulm, Germany
7. Ghattas J, Peleg M, Soffer P, Denekamp Y (2009) Learning the context of a clinical process.
In: Proceedings of the workshop on health-care processes (PROHealth 2009), Ulm, Germany
8. Ghattas J, Soffer P, Peleg M (2009) A formal model for process context learning. In:
Proceedings of the 5th international workshop on business process intelligence (BPI 2009),
Ulm, Germany
9. Guenther C Rinderle-Ma S, Reichert M, van der Aalst WMP, Recker J (2008) Using pro-
cess mining to learn from process changes in evolutionary systems. Int J Business Process
Integration Manage 3(1):61-78
10. Hammer M, Champy J (1994) Reengineering the corporation - a manifesto for business
revolution. Nicholas Brealey Publishing, London
 
 
 
 
 
 
 
 
 
 
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