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Data-driven AI for Self-adaptive Information Systems

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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.

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Data-driven AI for Self-adaptive Information Systems

  1. 1. Data-driven AI for Self-adaptive Information Systems Andreas Metzger BIOC & FAiSE Roma, MMXIX
  2. 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. 3. Need for Self-Adaptation 3 Engineering Requirements Engineering Design Coding Deployment Operations Manual information system engineering too slow… BIOC/FAiSE, Roma, MMXIX
  4. 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. 5. Data-driven AI as Enabling Technique BIOC/FAiSE, Roma, MMXIX 5 Image: https://www.datarevenue.com/ a.k.a Data-driven AI
  6. 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. 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. 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. 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. 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. 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. 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. 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. 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. 15. Results 15BIOC/FAiSE, Roma, MMXIX “Cheap” adaptation “Expensive” adaptation Static checkpoint Dynamic approach No proactive adaptation
  16. 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. 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. 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. 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. 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. 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. 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. 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. 24. Results 24 Stable Workload Off/on workload BIOC/FAiSE, Roma, MMXIX Growing workload Cyclic workload red = avg. reward blue = workload
  25. 25. Results 25 Real-world workload BIOC/FAiSE, Roma, MMXIX
  26. 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. 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. 28. Limitations Risks • Adaptations may “harm” environment (embedded systems / CPS) • Adversaries may manipulate training data Skills 28BIOC/FAiSE, Roma, MMXIX (Source: IDC) Year Gap (total EU) 2020 (baseline) 530,000 2020 (high-growth) 3,500,000
  29. 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. 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. 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

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