article

The next evolution of MDE: a seamless integration of machine learning into domain modeling

Authors:

Thomas Hartmann,

Francois Fouquet,

Yves Le TraonAuthors Info & Claims

Software and Systems Modeling (SoSyM), Volume 18, Issue 2

Pages 1285 - 1304

https://doi.org/10.1007/s10270-017-0600-2

Published: 01 April 2019 Publication History

Abstract

Machine learning algorithms are designed to resolve unknown behaviors by extracting commonalities over massive datasets. Unfortunately, learning such global behaviors can be inaccurate and slow for systems composed of heterogeneous elements, which behave very differently, for instance as it is the case for cyber-physical systems and Internet of Things applications. Instead, to make smart decisions, such systems have to continuously refine the behavior on a per-element basis and compose these small learning units together. However, combining and composing learned behaviors from different elements is challenging and requires domain knowledge. Therefore, there is a need to structure and combine the learned behaviors and domain knowledge together in a flexible way. In this paper we propose to weave machine learning into domain modeling. More specifically, we suggest to decompose machine learning into reusable, chainable, and independently computable small learning units, which we refer to as microlearning units. These microlearning units are modeled together with and at the same level as the domain data. We show, based on a smart grid case study, that our approach can be significantly more accurate than learning a global behavior, while the performance is fast enough to be used for live learning.

References

[1]

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint (2016). arXiv:1603.04467

[2]

Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., Riboni, D.: A survey of context modelling and reasoning techniques. Pervasive Mob. Comput. 6(2), 161---180 (2010).

Digital Library

[3]

Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learn. Res. 11, 1601---1604 (2010)

Digital Library

[4]

Bishop, C.M.: Model-based machine learning. Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci. 371(1984) (2012). http://rsta.royalsocietypublishing.org/content/371/1984/20120222

[5]

Budinsky, F., Steinberg, D., Ellersick, R.: Eclipse Modeling Framework: A Developer's Guide (2003)

Digital Library

[6]

Carstoiu, D., Cernian, A., Olteanu, A.: Hadoop hbase-0.20. 2 performance evaluation. In: 2010 4th International Conference on New Trends in Information Science and Service Science (NISS), pp. 84---87. IEEE (2010)

[7]

Chen, P.P.S.: The entity-relationship model--toward a unified view of data. ACM Trans. Database Syst. 1(1), 9---36 (1976).

Digital Library

[8]

Choetkiertikul, M., Dam, H.K., Tran, T., Ghose, A.: Predicting delays in software projects using networked classification. In: 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 353---364. IEEE (2015)

Digital Library

[9]

Daly, C.: Emfatic language reference (2004)

[10]

Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '00, pp. 71---80. ACM, New York, NY, USA (2000).

Digital Library

[11]

Durgesh, K.S., Lekha, B.: Data classification using support vector machine. J. Theor. Appl. Inf. Technol. 12(1), 1---7 (2010)

[12]

Esbensen, K.H., Geladi, P.: Principles of proper validation: use and abuse of re-sampling for validation. J. Chemom. 24(3---4), 168---187 (2010).

[13]

Fink, C.R., Chou, D.S., Kopecky, J.J., Llorens, A.J.: Coarse- and fine-grained sentiment analysis of social media text. Johns Hopkins APL Tech. Dig. 30(1), 22---30 (2011)

[14]

Fouquet, F., Nain, G., Morin, B., Daubert, E., Barais, O., Plouzeau, N., Jézéquel, J.: Kevoree modeling framework (KMF): efficient modeling techniques for runtime use. CoRR (2014). arxiv:1405.6817

[15]

Gerbessiotis, A., Valiant, L.: Direct bulk-synchronous parallel algorithms. J. Parallel Distrib. Comput. 22(2), 251---267 (1994). http://www.sciencedirect.com/science/article/pii/S0743731584710859

Digital Library

[16]

Group, O.M.: Tech. rep

[17]

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10---18 (2009).

Digital Library

[18]

Han, W., Miao, Y., Li, K., Wu, M., Yang, F., Zhou, L., Prabhakaran, V., Chen, W., Chen, E.: Chronos: A graph engine for temporal graph analysis. In: Proceedings of the Ninth European Conference on Computer Systems, EuroSys '14, pp. 1:1---1:14. ACM, New York, NY, USA (2014).

Digital Library

[19]

Hartmann, T., Fouquet, F., Klein, J., Traon, Y.L., Pelov, A., Toutain, L., Ropitault, T.: Generating realistic smart grid communication topologies based on real-data. In: 2014 IEEE International Conference on Smart Grid Communications, SmartGridComm 2014, Venice, Italy, November 3---6, 2014, pp. 428---433 (2014).

[20]

Hartmann, T., Fouquet, F., Nain, G., Morin, B., Klein, J., Barais, O., Traon, Y.L.: A native versioning concept to support historized models at runtime. In: Model-Driven Engineering Languages and Systems--17th International Conference, MODELS 2014, Valencia, Spain, September 28---October 3, 2014. Proceedings, pp. 252---268 (2014).

[21]

Hartmann, T., Fouquet, F., Nain, G., Morin, B., Klein, J., Traon, Y.L.: Reasoning at runtime using time-distorted contexts: A [email protected] based approach. In: The 26th International Conference on Software Engineering and Knowledge Engineering, Hyatt Regency, Vancouver, BC, Canada, July 1---3, 2013., pp. 586---591 (2014)

[22]

Hartmann, T., Moawad, A., Fouquet, F., Nain, G., Klein, J., Traon, Y.L.: Stream my models: reactive peer-to-peer distributed [email protected]. In: 18th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MoDELS 2015, Ottawa, ON, Canada, September 30---October 2, 2015, pp. 80---89 (2015).

[23]

Hartmann, T., Moawad, A., Fouquet, F., Reckinger, Y., Mouelhi, T., Klein, J., Le Traon, Y.: Suspicious electric consumption detection based on multi-profiling using live machine learning. In: 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm) (2015)

[24]

Henricksen, K., Indulska, J., Rakotonirainy, A.: Modeling context information in pervasive computing systems. In: Proceedings of the First International Conference on Pervasive Computing, Pervasive '02, pp. 167---180. Springer, London (2002). http://dl.acm.org/citation.cfm?id=646867.706693

Digital Library

[25]

Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5---53 (2004).

Digital Library

[26]

Hido, S., Tokui, S., Oda, S.: Jubatus: An open source platform for distributed online machine learning. In: NIPS 2013 Workshop on Big Learning, Lake Tahoe (2013)

[27]

Hug, T., Lindner, M., Bruck, P.A.: Microlearning: emerging concepts, practices and technologies after e-learning. In: Proceedings of Microlearning, vol. 5 (2005)

[28]

Kent, S.: Model driven engineering. In: Proceedings of the Third International Conference on Integrated Formal Methods, IFM '02, pp. 286---298. Springer, London (2002). http://dl.acm.org/citation.cfm?id=647983.743552

Digital Library

[29]

Kohtes, R.: From Valence to Emotions: How Coarse Versus Fine-Grained Online Sentiment Can Predict Real-World Outcomes. Anchor Academic Publishing, Hamburg (2014)

[30]

Lassila, O., Swick, R.R.: Resource Description Framework (RDF) Model and Syntax Specification. W3c recommendation, W3C (1999)

[31]

Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A., Hellerstein, J.M.: Distributed graphlab: a framework for machine learning and data mining in the cloud. Proc. VLDB Endow. 5(8), 716---727 (2012)

Digital Library

[32]

Low, Y., Gonzalez, J.E., Kyrola, A., Bickson, D., Guestrin, C., Hellerstein, J.M.: Graphlab: a new framework for parallel machine learning. CoRR (2014). arxiv:1408.2041

Digital Library

[33]

Meta object facility (MOF) 2.5 core specification (2015). Version 2.5

[34]

Miorandi, D., Sicari, S., De Pellegrini, F., Chlamtac, I.: Internet of things: vision, applications and research challenges. Ad Hoc Netw. 10(7), 1497---1516 (2012)

Digital Library

[35]

Moawad, A.: Towards ambient intelligent applications using [email protected] and machine learning for context-awareness. Ph.D. thesis, University of Luxembourg (2016)

[36]

Morin, B., Barais, O., Jezequel, J.M., Fleurey, F., Solberg, A.: [email protected] to support dynamic adaptation. Computer 42(10), 44---51 (2009).

Digital Library

[37]

Norvig, P.: Artificial Intelligence. NewScientist (27) (2012)

[38]

Object Management Group: OMG Unified Modeling Language, Version 2.5. http://www.omg.org/spec/UML/2.5/PDF (2015)

[39]

Ohmann, T., Herzberg, M., Fiss, S., Halbert, A., Palyart, M., Beschastnikh, I., Brun, Y.: Behavioral resource-aware model inference. In: Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering, pp. 19---30. ACM (2014)

Digital Library

[40]

Ottensooser, A., Fekete, A., Reijers, H.A., Mendling, J., Menictas, C.: Making sense of business process descriptions: an experimental comparison of graphical and textual notations. J. Syst. Softw. 85(3), 596---606 (2012)

Digital Library

[41]

Rothenberg, J.: Artificial intelligence, simulation and modeling. In: The Nature of Modeling, pp. 75---92. Wiley, New York (1989). http://dl.acm.org/citation.cfm?id=73119.73122

Digital Library

[42]

Sun, J., Faloutsos, C., Papadimitriou, S., Yu, P.S.: Graphscope: parameter-free mining of large time-evolving graphs. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '07, pp. 687---696. ACM, New York, NY, USA (2007).

Digital Library

[43]

Sutcliffe, A., Sawyer, P.: Requirements elicitation: towards the unknown unknowns. In: Requirements Engineering Conference (RE), 2013 21st IEEE International, pp. 92---104. IEEE (2013)

[44]

Vierhauser, M., Rabiser, R., Grunbacher, P., Egyed, A.: Developing a DSL-based approach for event-based monitoring of systems of systems: experiences and lessons learned. In: 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 715---725. IEEE (2015)

Digital Library

[45]

W3C, W.W.W.C.: Owl 2 web ontology language. structural specification and functional-style syntax (2009)

[46]

Wernick, M.N., Yang, Y., Brankov, J.G., Yourganov, G., Strother, S.C.: Machine learning in medical imaging. IEEE Signal Process. Mag. 27(4), 25---38 (2010).

[47]

Yuan, N.J., Zheng, Y., Zhang, L., Xie, X.: T-finder: a recommender system for finding passengers and vacant taxis. IEEE Trans. Knowl. Data Eng. 25(10), 2390---2403 (2013)

Digital Library

[48]

Zhang, B., Zhang, L.: Multi-granular representation-the key to machine intelligence. In: 3rd International Conference on Intelligent System and Knowledge Engineering, 2008. ISKE 2008, vol. 1, pp. 7---7 (2008).

[49]

Zhang, B., Zhang, L.: Multi-granular representation-the key to machine intelligence. In: 3rd International Conference on Intelligent System and Knowledge Engineering, 2008. ISKE 2008, vol. 1, pp. 7---7. IEEE (2008)

[50]

Zhu, H., Shan, L., Bayley, I., Amphlett, R.: Formal descriptive semantics of uml and its applications. In: UML 2 Semantics and Applications p. 95 (2009)

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The next evolution of MDE: a seamless integration of machine learning into domain modeling
1. Computing methodologies
  1. Machine learning
2. Software and its engineering

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cover image Software and Systems Modeling (SoSyM)

Software and Systems Modeling (SoSyM) Volume 18, Issue 2

Apr 2019

766 pages

ISSN:1619-1366

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Copyright © Copyright © 2019 Springer-Verlag GmbH Germany, part of Springer Nature.

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Springer-Verlag

Berlin, Heidelberg

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Published: 01 April 2019

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Cited By

Tabbiche MKhalfi MAdjoudj R(2023)Applying Machine Learning and Model-Driven Approach for the Identification and Diagnosis Of Covid-19International Journal of Distributed Systems and Technologies10.4018/IJDST.32164814:1(1-27)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.4018/IJDST.321648
Lyan GJézéquel JGross-Amblard DLefeuvre RCombemale B(2023)Reasoning over time into models with DataTimeSoftware and Systems Modeling (SoSyM)10.1007/s10270-023-01080-x22:5(1689-1712)Online publication date: 10-Jan-2023
https://dl.acm.org/doi/10.1007/s10270-023-01080-x
Kirchhof JKusmenko ERitz JRumpe BMoin ABadii AGünnemann SChallenger MKühn TSousa V(2022)MDE for machine learning-enabled software systemsProceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings10.1145/3550356.3561576(380-387)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1145/3550356.3561576
Lorentz JHartmann TMoawad AFouquet FAouada DLe Traon Y(2022)CalcGraph: taming the high costs of deep learning using modelsSoftware and Systems Modeling (SoSyM)10.1007/s10270-022-01052-722:4(1151-1174)Online publication date: 25-Oct-2022
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