Artificial Intelligence (AI) offers tremendous potential to benefit citizens, economy and society. From an industrial point of view, AI means algorithm-based and data-driven information systems that provide machines and people with digital capabilities such as perception, reasoning, learning and even autonomous decision making. AI thereby facilitates information systems to draw conclusions, learn, adapt and adjust parameters accordingly. With recent advances in special-purpose hardware and machine learning algorithms, AI is capable of capturing more and more complex problems. This keynote talk will explore the opportunities that AI offers in building self-adaptive information systems. On the one hand, it will present how ensembles of deep neural networks support proactive decision making for business process execution. On the other hand, it will present how reinforcement learning may be employed to build self-learning information systems. These systems learn from every interaction with their environment in order to dynamically improve themselves during operations. The talk closes with a critical look at the challenges entailed in delivering responsible AI-based information systems.

1. Data-driven AI for
Self-adaptive
Information Systems
Andreas Metzger
BIOC & FAiSE
Roma, MMXIX

2. Agenda
2
Motivation and
Background
Case 1:
Deep Learning for
Proactive Process
Adaptation
Case 2:
Policy-based Reinforcement
Learning for
Self-adaptive
Cloud Services
Discussion
and Outlook
BIOC/FAiSE, Roma, MMXIX

3. Need for Self-Adaptation
3
Engineering
Requirements
Engineering
Design
Coding
Deployment
Operations
Manual information system engineering too slow…
BIOC/FAiSE, Roma, MMXIX

4. Need for Self-Adaptation
4
Requirements
Engineering
Design
Coding
Deployment
Operations
Monitoring
Adaptation
Self-Adaption Engineering
Automated perception, reasoning, actuation, …
BIOC/FAiSE, Roma, MMXIX

5. Data-driven AI as Enabling Technique
BIOC/FAiSE, Roma, MMXIX 5
Image: https://www.datarevenue.com/
a.k.a
Data-driven AI

6. Data-driven as Enabling Technique
Main Drivers
BIOC/FAiSE, Roma, MMXIX 6
2020
2011
44 Zettabyte
1 Zettabyte
Big Data
Hardware
e.g., GPU / TPU
Algorithms
e.g., Deep Learning

7. Case 2Case 1
Data-driven as Enabling Technique
BIOC/FAiSE, Roma, MMXIX 7
Unsupervised
Learning
Supervised
Learning
Reinforcement
Learning
No Feedback Labelled Training Data Reward for an Action

8. Agenda
8
Motivation and
Background
Case 1:
Deep Learning for
Proactive Process
Adaptation
Case 2:
Policy-based Reinforcement
Learning for
Self-adaptive
Cloud Services
Discussion
and Outlook
BIOC/FAiSE, Roma, MMXIX

9. Process
completiontCheckpoint j
Process
start
Proactive Process Adaptation “in a Nutshell”
BIOC/FAiSE, Roma, MMXIX 9
Monitor
Predict
Proactive
adaptation
planned /
acceptable situations
= Violation
= Non-
Violation

E.g., Delayed
freight delivery
E.g., Schedule air
instead of land
transport
E.g., Freight
delivery within 2
days
Process
Performance

10. Considerations for Proactive Adaptation
Accuracy
• False violations  Unnecessary adaptations
• False non-violations  Missed adaptations
Earliness
• Late prediction  no time for adaptation
But: Trade-off between accuracy and earliness
BIOC/FAiSE, Roma, MMXIX 10
[Metzger & Neubauer, 2018] [Teinemaa et al., 2019]
BPIC 2017BPIC 2012Cargo 2000
Process
completion
Accuracy[MCC]
Accuracy[AUC]

11. Prediction Reliability for Proactive Adaptation
BIOC/FAiSE, Roma, MMXIX 11
Reliability Estimate
• Probability that individual prediction is correct
 Distinguish between more or less reliable predictions
Dynamic Approach
• Use earliest prediction with sufficiently high reliability
Aggregate Accuracy
75%
75%
75%
75%
Prediction #
1
2
3
…
Reliability Estimate
60%
90%
70%
…

12. Prediction Reliability for Proactive Adaptation
Real-World Example
12BIOC/FAiSE, Roma, MMXIX
Alarm about Delay
Reliability Estimate
Terminal Productivity Cockpit
Data sources
• Data streams from terminal equipment
(1.3 mio states / month)
• Integrated data of container moves
(10,000 moves / month)
Container terminal

13. Prediction Reliability for Proactive Adaptation
Realization of Dynamic Approach
13BIOC/FAiSE, Roma, MMXIX
Process
monitoring
data at
Checkpoint j
RNN Model 1
RNN Model m
…
Ensemble
Prediction
[Tj = “non-violation”]
Proactive
Process
Adaptation
No Proactive
Process
Adaptation
Prediction Tj
Reliability
estimate j
[Tj =
“violation”]
[j  threshold]
[j < threshold]
Deep Learning

14. RNNs as Base Learners
RNN = Recurrent Neural Network
Benefits
• High accuracy [Tax et. al. 2017; Evermann et al. 2017, Metzger & Nebauer, 2018]
• Arbitrary length process instances (without sequence encoding)
• Predictions at any checkpoint
Scalability
• Long training time
 Parallelization (Bagging)
 Hardware speedups
14BIOC/FAiSE, Roma, MMXIX
Hardware type Training
time
CPU 25 min
GPU (Nvidia CuDNN) 8 min
Google TPU (Tensorflow) 2 min

15. Results
15BIOC/FAiSE, Roma, MMXIX
“Cheap”
adaptation
“Expensive”
adaptation
Static checkpoint
Dynamic approach
No proactive
adaptation

16. Results
16BIOC/FAiSE, Roma, MMXIX
“Cheap”
adaptation
“Expensive”
adaptation
Static checkpoint
Dynamic approach
No proactive
adaptation
Details in A. Metzger, A. Neubauer, P. Bohn, and K. Pohl, “Proactive process adaptation
using deep learning ensembles,” in 31st Int’l Conference on Advanced Information
Systems Engineering (CAiSE 2019), Rome, Italy, June 3-7, 2019, ser. LNCS, P. Giorgini and B.
Weber, Eds., vol. 11483. Springer, 2019.
Open Access: https://link.springer.com/chapter/10.1007%2F978-3-030-21290-2_34

17. Agenda
17
Motivation and
Background
Case 1:
Deep Learning for
Proactive Process
Adaptation
Case 2:
Policy-based Reinforcement
Learning for
Self-adaptive
Cloud Services
Discussion
and Outlook
BIOC/FAiSE, Roma, MMXIX

18. Design Time Uncertainty
• What are potential
environment situations?
• How does adaptation
affect quality requirements?
Learning for Self-Adaptation „in a Nutshell“
18
Self-Adaptation Logic
Knowledge
Analyze Plan
Monitor Execute
System Logic
Environment
Solution
Self-Adaptation Logic
Knowledge
Analyze Plan
Monitor Execute
System Logic
Environment
Online Learning
Observe Induce
Self-adaptive System Reference Model
BIOC/FAiSE, Roma, MMXIX

19. Learning for Self-Adaptation „in a Nutshell“
Most widely used: Reinforcement Learning
19BIOC/FAiSE, Roma, MMXIX
Agent
(with action-
selection policy )
Environment
Action
atrt+1
st+1
State
st
Reward
rt
https://www.youtube.com/watch?v=gn4nRCC9TwQ

20. Considerations for Self-adaptive Cloud Services
Large, continuous environment space
• Continuous state variables (e.g., workload)
• Many state variables
State of the Art: Value-based RL
• Value function =
expected cumulative reward per state
• Requires manual quantization of states
 error prone
• Requires balancing exploration vs. exploitation
20BIOC/FAiSE, Roma, MMXIX
[Marquezan et al., 2014]

21. Coping with Large Environment Space
Policy-based reinforcement learning
• Approximates and
generalizes over states
• Probabilistic action selection
Learning via policy gradient methods
• Policy update according to gradient of objective function
21
Environment
Action
at
rt+1
st+1
State
st
Reward
rt
BIOC/FAiSE, Roma, MMXIX
Weights of neural
network

22. Coping with Large Environment Space
Realization of policy-based RL for self-adaptive cloud services
22
Self-Adaptation Logic
Knowledge
Analyze Plan
Monitor Execute
BIOC/FAiSE, Roma, MMXIX
Self-Adaptation Logic
Knowledge
Analyze Plan
Monitor Execute
Policy-based Reinforcement Learning
at
rt+1
t+1
Monitor Execute
Action-
Selection
Self-Adaptation Logic
Policy 
(Knowledge)
st
Policy Update
st+1

23. Coping with Large Environment Space
Benchmark Example
State
Reward
23BIOC/FAiSE, Roma, MMXIX
[Klein et al., 2014]Brownout-RUBiS
Nbr Requests
CPU Load
Actual Recomm. Rate
Response time (ms)
Auction Web Application
• User requests served with
recommendations for
similar products
Action
Set dimmer value
= per-request probability that
recommendation engine is activated

24. Results
24
Stable Workload
Off/on workload
BIOC/FAiSE, Roma, MMXIX
Growing workload
Cyclic workload
red = avg. reward
blue = workload

25. Results
25
Real-world workload
BIOC/FAiSE, Roma, MMXIX

26. Agenda
26
Motivation and
Background
Case 1:
Deep Learning for
Proactive Process
Adaptation
Case 2:
Policy-based Reinforcement
Learning for
Self-adaptive
Cloud Services
Discussion
and Outlook
BIOC/FAiSE, Roma, MMXIX

27. Lessons Learned
Deep learning requires little hyper-parametrization
• If enough good quality data is available / can be collected
Use deep learning to increase productivity
of information system engineering
Prediction reliability offers additional decision support
• Operators put more trust in predictions
• Cost savings
Augment predictions with reliability estimates, confidence intervals,
error ranges, etc.
Data quality is a key concern
• “Garbage in – garbage out”
• Missing data, data accuracy, timeliness, timestamps (clocks), …
• Very resource and time-intensive
Plan sufficient time and effort for data quality, data integration and
refinement of data collection
27BIOC/FAiSE, Roma, MMXIX

28. Limitations
Risks
• Adaptations may “harm” environment
(embedded systems / CPS)
• Adversaries may manipulate training data
Skills
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(Source: IDC)
Year Gap (total EU)
2020 (baseline) 530,000
2020 (high-growth) 3,500,000

29. Outlook
29BIOC/FAiSE, Roma, MMXIX
Decision Making
• Deep learning for high
accuracy descriptive and
predictive analytics
• Reinforcement learning
for solving complex
planning and decision
problems
Enactment
• Actuation driven by AI
decisions
• Safety and
trustworthiness as key
requirements for
adoption

30. Thanks!
BIOC/FAiSE, Roma, MMXIX 30
Research leading to these results has received
funding from the EU’s Horizon 2020 research and
innovation programme under grant agreements no.
731932 – http://www.transformingtransport.eu
732630 – http://www.big-data-value.eu
780351 – https://enact-project.eu

31. References
[Evermann et al. 2017] Evermann, J., Rehse, J., Fettke, P.: Predicting process behaviour using
deep learning. Decision Support Systems 100 (2017)
[Klein et al., 2014] Klein, C., Maggio, M., Arz´ en, K.E., Hernández-Rodriguez, F.: Brownout:
Building more robust cloud applications. In: 36th Intl Conf. on Software Engineering
(ICSE 2014). pp. 700–711. ACM (2014)
[Metzger & Neubauer, 2018] A. Metzger and A. Neubauer, “Considering non-sequential
control flows for process prediction with recurrent neural networks,” in 44th Euromicro
Conference on Software Engineering and Advanced Applications (SEAA 2018), Prague,
Czech Republic, August 29-31, 2018, T. Bures and L. Angelis, Eds. IEEE Computer
Society, 2018, pp. 268–272.
[Metzger et al., 2019] A. Metzger, A. Neubauer, P. Bohn, and K. Pohl, “Proactive process
adaptation using deep learning ensembles,” in 31st Int’l Conference on Advanced
Information Systems Engineering (CAiSE 2019), Rome, Italy, June 3-7, 2019, ser. LNCS, P.
Giorgini and B. Weber, Eds., vol. 11483. Springer, 2019. Open Access:
https://link.springer.com/chapter/10.1007%2F978-3-030-21290-2_34
[Tax et. al. 2017] Tax, N., Verenich, I., Rosa, M.L., Dumas, M.: Predictive business process
monitoring with LSTM neural networks. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017, Essen,
Germany, June 12-16, 2017. LNCS, vol. 10253. Springer (2017)
[Teinemaa et al. 2019] Teinemaa, I., Dumas, M., Rosa, M.L., Maggi, F.M.: Outcome-oriented
predictive process monitoring: Review and benchmark. ACM Transactions on
Knowledge Discovery from Data (TKDD) 13 (2019)
BIOC/FAiSE, Roma, MMXIX 31