![KSRI4
Those challenges for comprehensive analytics in smart
service systems can be categorized
Technical
challenges
Organizational
challenges
Algorithmic
challenges
Enabling continuous
learning
(Lee et al., 2014)
Allowing flexibility &
modularity
(Wielki, 2013)
Handling of data
heterogeneity
(Kaufmann et al., 2005,
Baars & Kemper, 2008)
Preservation of IP &
data confidentiality [11]
(Jensen, 2013)
Processing of distributed
data sources
(Lucke et al., 2008)
Achieving (superior)
predictions
Enabling robust
predictions
(Saar-Tsechansky & Provost,
2007)
Mapping of time
dependencies
01/04/2018 Robin Hirt - Service-oriented Cognitive Analytics - 51st Hawaii International Conference on System Service (HICSS)](https://image.slidesharecdn.com/metalearning-hirt-slideshare-180104211151/85/Service-oriented-Cognitive-Analytics-in-Smart-Service-Systems-4-320.jpg)
![KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI)
www.kit.edu
www.ksri.kit.edu
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
Karlsruhe Service Research Institute (KSRI)
Kaiserstraße 89
D-76133 Karlsruhe
[T] +49 721 45758
[F] +49 721 45655
[M] office@ksri.kit.edu
Robin Hirt, M.Sc.
hirt@kit.edu
linkedin.com/in/robinhirt
robin_hirt
Get in touch!](https://image.slidesharecdn.com/metalearning-hirt-slideshare-180104211151/85/Service-oriented-Cognitive-Analytics-in-Smart-Service-Systems-12-320.jpg)
Service-oriented Cognitive Analytics in Smart Service Systems
- 1. KIT – The Research University in the Helmholtz Association KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI) www.ksri.kit.edu Service-oriented Cognitive Analytics in Smart Service Systems: A Research Agenda Robin Hirt, Niklas Kühl, Gerhard Satzger, Björn Schmitz 4th January 2018 – 51st Hawaii International Conference on System Service (HICSS) 1. Motivation 2. Challenges 3. Related Work 4. Cognitive Systems 5. Meta Learning 6. Status Quo 7. Round-up
- 2. KSRI2 In today’s connected world, data is distributed across different entities in a smart service system Machine operator / Manufacturer Machine producer Component producer BComponent producer A Output machine station Input Supplier How can comprehensive analyses be performed across company borders? Robin Hirt - Service-oriented Cognitive Analytics - 51st Hawaii International Conference on System Service (HICSS)01/04/2018
- 3. KSRI3 Comprehensive analytics in smart service systems faces various challenges (excerpt)… 01/04/2018 Central model “Central” entity + + Data volume: Data sources might be too large to be transmitted in distributed settings x Entity A Entity B Entity C ⚡️ ⚡️ ⚡️ Data heterogeneity: Every entity might produce different (types) of data, which requires customized processing Data confidentiality: The fear of the exposure of sensitive data prevents entities from sharing data Data describing internal processes or interaction Machine learning models solving a business problemInsight or prediction Simplified smart service system Robin Hirt - Service-oriented Cognitive Analytics - 51st Hawaii International Conference on System Service (HICSS)
- 4. KSRI4 Those challenges for comprehensive analytics in smart service systems can be categorized Technical challenges Organizational challenges Algorithmic challenges Enabling continuous learning (Lee et al., 2014) Allowing flexibility & modularity (Wielki, 2013) Handling of data heterogeneity (Kaufmann et al., 2005, Baars & Kemper, 2008) Preservation of IP & data confidentiality [11] (Jensen, 2013) Processing of distributed data sources (Lucke et al., 2008) Achieving (superior) predictions Enabling robust predictions (Saar-Tsechansky & Provost, 2007) Mapping of time dependencies 01/04/2018 Robin Hirt - Service-oriented Cognitive Analytics - 51st Hawaii International Conference on System Service (HICSS)
- 5. KSRI5 Related work is focusing on solving on a limited problem space problems (excerpt) Robustness Predictionperformance Timedependencies Dataheterogeneity Flexibilityandmodularity Continuouslearning Distributeddatasources IP&privacypreservation Fog computing ○ ◐ ○ ● ● ○ ● ○ Service-oriented decision support ○ ○ ◐ ○ ● ○ ◐ ○ Complex event processing ○ ◐ ● ◐ ◐ ○ ● ○ Privacy-preserving data mining ○ ○ ○ ○ ○ ○ ◐ ● Service-oriented cognitive analytics ● ● ● ● ● ● ● ● 01/04/2018 We strive to solve challenges for analytics in smart service systems using a cognitive paradigm ●fullyaddressed◐partiallyaddressed○notaddressed Robin Hirt - Service-oriented Cognitive Analytics - 51st Hawaii International Conference on System Service (HICSS)
- 6. KSRI6 The cognitive paradigm can enable innovation in information systems Human cognition (“psychological view”): “(…) all processes by which the sensory input is transformed, reduced, elaborated, stored, recovered, and used” (Neisser, 1967, p. 4) Cognitive computing (“computer engineering view”): “(…) aims to develop a coherent, unified, universal mechanism inspired by the mind's capabilities” (Modha et al., 2011) Cognitive systems (”information systems view”): systems that mimic the human mind’s capabilities (Hirt et al., forthcoming) Pre-analyses Comprehensive analysis Heterogeneous data E.g., ability to perform comprehensive analyses: We are able to mimic the ability to perform comprehensive analyses using meta machine learning (Hirt R., Kühl, N., 2017) 01/04/2018 Robin Hirt - Service-oriented Cognitive Analytics - 51st Hawaii International Conference on System Service (HICSS)
- 7. KSRI7 Meta learning enables to perform aggregated predictions on separate data sets Meta learning aims to enable “learning about learning” (Džeroski and Ženko, 2004), e.g., ensembles (Bagging & Boosting) (Breiman, 1996, Freund and Schapire, 1996) or Stacked Generalization (Wolpert, 1992) ML 1 ML 2 ML 3 e.g. Vote Single-source Conventional ensemble in single-source settings ML 1 ML 2 ML 3 MML D1 D2 D3 Multi-source vs. “Cognitive” ensemble in distributed settings e.g., stacked generalization 01/04/2018 Goal: Better predictions Goal: Comprehensive analyses + better predictions Robin Hirt - Service-oriented Cognitive Analytics - 51st Hawaii International Conference on System Service (HICSS)
- 8. KSRI8 Analytics for smart service systems needs to be flexible and able to adapt to new constellations 01/04/2018 Robin Hirt - Service-oriented Cognitive Analytics - 51st Hawaii International Conference on System Service (HICSS)
- 9. KSRI9 Transferring the SOA paradigm to enable flexible & modular analyses in distributed service systems Data describing internal processes or interaction Machine learning models solving a business problemInsight or prediction interface Subordinate entity 1 interface Subordinate entity 2 interface Subordinate entity n … interface Central “cognitive” entity interface e.g., business service 01/04/2018 ✅ Transferred data drastically decreased ✅ Custom models for each data type ✅ No exposure of raw data Robin Hirt - Service-oriented Cognitive Analytics - 51st Hawaii International Conference on System Service (HICSS)
- 10. KSRI10 Diverse projects involving cognitive meta analyses show its feasibility in different domains (excerpt) Predicting production line quality Gender prediction of Twitter users (gender-prediction.science) 01/04/2018 Current project Predicting the quality of assembled goods by combining heterogeneous operational and supply data through meta machine learning Robin Hirt - Service-oriented Cognitive Analytics - 51st Hawaii International Conference on System Service (HICSS)
- 11. KSRI11 In summary, service-oriented cognition may be a step change for system-wide analytics The combination of the SOA paradigm and cognitive analyses through meta machine learning enables comprehensive analyses in smart service systems System view on analytics across company boarders yields various “practical” challenges, especially the transformation of organization and mindset ( “systems view”) Despite comprehensive analyses, another issue for analytics in smart service systems is the exchange/transfer of analytical knowledge or precise models Outlook How does the proposed approach perform in a real-world use case? Which problems appear? How does it affect service systems? How can we further enhance the cognitive paradigm (e.g., transfer learning)? Thank you for your attention! 01/04/2018 Robin Hirt - Service-oriented Cognitive Analytics - 51st Hawaii International Conference on System Service (HICSS)
- 12. KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI) www.kit.edu www.ksri.kit.edu KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft Karlsruhe Service Research Institute (KSRI) Kaiserstraße 89 D-76133 Karlsruhe [T] +49 721 45758 [F] +49 721 45655 [M] office@ksri.kit.edu Robin Hirt, M.Sc. hirt@kit.edu linkedin.com/in/robinhirt robin_hirt Get in touch!
- 13. KSRI13 References 1/2 Neisser, U. (1967). Cognitive Psychology. Thinkingjudgement and decision making. Modha, D. S., R. Ananthanarayanan, S. K. Esser, A. Ndirango, A. J. Sherbondy and R. Singh. (2011). “Cognitive computing.” Communications of the ACM, 54(8), 62. Hirt, R., Kühl, N., Satzger, G., Cognition in the Era of Smart Service Systems, forthcoming H. Chen and V. C. Storey, “Business Intelligence and analytics : From Big Data To Big Impact,” Mis Q., vol. 36, no. 4, pp. 1165–1188, 2012. T. H. Davenport, “Competing on analytics,” Harv. Bus. Rev., vol. 84, no. 1, p. 98, 2006. S. Barile and F. Polese, “Smart Service Systems and Viable Service Systems: Applying Systems Theory to Service Science,” Serv. Sci., vol. 2, no. 1–2, pp. 21–40, 2010. C. Chong, S. P. Kumar, and S. Member, “Sensor Networks: Evolution, Opportunities and Challenges,” vol. 91, no. 8, 2003. G. Allmendinger and R. Lombreglia, “Four strategies for the age of smart services,” Harv. Bus. Rev., vol. 83, no. 10, pp. 131–145, 2005. J. Lee, H. A. Kao, and S. Yang, “Service innovation and smart analytics for Industry 4.0 and big data environment,” Procedia CIRP, vol. 16, pp. 3–8, 2014. D. Miorandi, S. Sicari, F. De Pellegrini, and I. Chlamtac, “Internet of things: Vision, applications and research challenges,” Ad Hoc Networks, vol. 10, no. 7, pp. 1497– 1516, 2012. M. Cannataro, A. Congiusta, A. Pugliese, D. Talia, and P. Trunfio, “Distributed data mining on grids: Services, tools, and applications,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. 34, no. 6, pp. 2451–2465, 2004. D. Lucke, C. Constantinescu, and E. Westkämper, “Smart Factory - A Step towards the Next Generation of Manufacturing,” in Manufacturing Systems and Technologies for the New Frontier, M. Mitsuishi, K. Ueda, and F. Kimura, Eds. London: Springer London, 2008, pp. 115–118. Y. Lindell and B. Pinkas, “Privacy Preserving Data Mining,” Priv. Preserv. Data Min., pp. 1–25, 2000. M. Jensen, “Challenges of Privacy Protection in Big Data Analytics,” IEEE Int. Congr. Big Data, pp. 235–238, 2013. R. Fay, U. Kaufmann, A. Knoblauch, H. Markert, and G. Palm, “Combining visual attention, object recognition and associative information processing in a NeuroBotic system,” Lect. Notes Computer Science, vol. 3575 LNAI, pp. 118–143, 2005. H. Baars and H.-G. Kemper, “Management Support with Structured and Unstructured Data - An Integrated Business Intelligence Framework,” Inf. Syst. Manag., vol. 25, no. 2, pp. 132–148, 2008. J. Wielki, “Implementation of the Big Data concept in organizations – Possibilities, impediments and challenges,” Proc. Fed. Conf. Comput. Sci. Inf. Syst., no. September 2013, pp. 985–989, 2013. M. Saar-Tsechansky and F. Provost, “Handling Missing Values when Applying Classification Models,” J. Mach. Learn. Res., vol. 8, pp. 1625–1657, 2007. F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog Computing and Its Role in the Internet of Things,” Proc. first Ed. MCC Work. Mob. cloud Comput., pp. 13– 16, 2012. H. Demirkan and D. Delen, “Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud,” Decis. Support Syst., vol. 55, no. 1, pp. 412–421, 2013. D. B. Robins, “Complex Event Processing,” 2010 Second Int. Work. Educ. Technol. Comput. Sci., p. 10, 2010. 01/04/2018 Robin Hirt - Service-oriented Cognitive Analytics - 51st Hawaii International Conference on System Service (HICSS)
- 14. KSRI14 References 2/2 R. Agrawal and R. Srikant, “Privacy-preserving data mining,” Proc. 2000 ACM SIGMOD Int. Conf. Manag. data - SIGMOD ’00, vol. 29, no. 2, pp. 439–450, 2000. D. S. Modha, R. Ananthanarayanan, S. K. Esser, A. Ndirango, A. J. Sherbondy, and R. Singh, “Cognitive computing,” Commun. ACM, vol. 54, no. 8, p. 62, 2011. J. Kludas, E. Bruno, and S. Marchand-Maillet, “Information Fusion in Multimedia Information Retrieval,” Adapt. Multimedial Retr. Retr. User Semant., vol. 4918, pp. 147–159, 2008. P. K. Atrey, M. A. Hossain, A. El Saddik, and M. S. Kankanhalli, Multimodal fusion for multimedia analysis: A survey, vol. 16, no. 6. 2010. J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng, “Multimodal Deep Learning,” Proc. 28th Int. Conf. Mach. Learn., pp. 689–696, 2011. T. G. Dietterich, “Machine-Learning Research,” AI Magazine, vol. 18, no. 4, p. 97, 1997. S. Džeroski and B. Ženko, “Is combining classifiers with stacking better than selecting the best one?,” Mach. Learn., vol. 54, no. 3, pp. 255–273, 2004. L. Todorovski and S. Džeroski, “Combining classifiers with meta decision trees,” Mach. Learn., vol. 50, no. 3, pp. 223–249, 2003. J. R. Quinlan, “Bagging, boosting, and C4.5,” Proc. Thirteen. Natl. Conf. Artif. Intell., vol. 5, no. Quinlan 1993, pp. 725–730, 2006. K. Ikeda, G. Hattori, C. Ono, H. Asoh, and T. Higashino, “Twitter user profiling based on text and community mining for market analysis,” Knowledge-Based Syst., vol. 51, pp. 35–47, 2013. J. Gama and P. Brazdil, “Cascade Generalization,” Mach. Learn., vol. 41, no. 3, pp. 315–343, 2000. C. Merz, “Using Correspondence Analysis to Combine Classifiers,” Mach. Learn., vol. 36, pp. 33–58, 1999. D. Rao, D. Yarowsky, A. Shreevats, and M. Gupta, “Classifying latent user attributes in twitter,” Proc. 2nd Int. Work. Search Min. user-generated contents - SMUC ’10, p. 37, 2010. B. Kuechler, V. Vaishnavi, and C. I. Systems, “Theory Development in Design Science Research: Anatomy of a Research Project,” Conf. Des. Sci. Res. Inf. Syst. Technol., pp. 1–15, 2007. K. Peffers, M. Rothenberger, T. Tuunanen, and R. Vaezi, “Design Science Research Evaluation,” Des. Sci. Res. Inf. Syst. Adv. Theory Pract., pp. 398–410, 2012. S. Gregor and A. R. Hevner, “Positioning and Presenting Design Science Types of Knowledge in Design Science Research,” MIS Q., vol. 37, no. 2, pp. 337– 355, 2013. 01/04/2018 Robin Hirt - Service-oriented Cognitive Analytics - 51st Hawaii International Conference on System Service (HICSS)