This dissertation addresses the above-mentioned challenges by extending the applica- bility of MDE and [email protected] to the domain of live analytics for CPSs. The hypothesis behind this dissertation is that complex and frequently changing data of CPSs can be efficiently, i.e., in near real-time, analysed by organising them in a data model connecting raw data, domain knowledge, and machine learning. This hypothesis is evaluated against a concrete smart grid case study. Figure 1.6 depicts the concrete contributions made in this thesis and shows which of the challenges described in Sec- tion 1.4 are addressed by each of these contributions. In the following, a short overview is provided about each contribution.
A temporal data model. The first contribution of this dissertation addresses the challenge of representing and storing temporal, i.e., continuously evolving data. There- fore, it defines a temporal data model together with a time-relative navigation concept. The proposed approach considers time as a first-class property crosscutting any context element and any relation between context elements. This contribution also defines an efficient storage concept for the proposed temporal data model. The goal of this ap- proach is to provide analytics with data structures to efficiently reason about massive amounts of continuously evolving data.
This contribution is based on the work that has been presented in the following papers:
• Thomas Hartmann, Fran¸cois Fouquet, Gr´egory Nain, Brice Morin, Jacques Klein, Olivier Barais, and Yves Le Traon. A native versioning concept to support histor- ized models at runtime. In Model-Driven Engineering Languages and Systems - 17th International Conference, MODELS 2014, Valencia, Spain, September 28 - October 3, 2014. Proceedings, pages 252–268, 2014
• Thomas Hartmann, Fran¸cois Fouquet, Gr´egory Nain, Brice Morin, Jacques Klein, and Yves Le Traon. Model-based time-distorted contexts for efficient temporal reasoning.
In The 26th International Conference on Software Engineering and Knowledge Engi- neering, SEKE 2014, Vancouver, BC, Canada, July 1-3, 2014., pages 746–747, 2014 (best paper award)
A multi-dimensional graph data model. The second contribution of this the- sis tackles the challenge of simultaneously exploring different hypothetical actions. It extends the temporal data model to a multi-dimensional data model able to reflect a large number of different alternatives. The suggested data model allows each alterna- tive to evolve independently with its own independent history in order to enable the simultaneous exploration of many different actions. This contribution aims to define an efficient data model able to enable what-if analysis for a large number of independent actions even on a massive amount of (temporal) data.
This contribution is based on the work that has been presented in the following paper:
• under submission at ACM/USENIX EuroSys 2017: Thomas Hartmann, Assaad
Moawad, Francois Fouquet, Gregory Nain, Romain Rouvoy, Yves Le Traon, and Jacques Klein. PIXEL: A Graph Storage to Support Large Scale What-If Analysis
A peer-to-peer distribution and stream processing model. A third contri- bution of this dissertation copes with the challenge of data analytics over massively distributed datasets of frequently changing data. It proposes an approach to trans- parently distribute the suggested data model in a peer-to-peer manner and defines a stream processing method to efficiently handle frequent changes. More specifically, it combines ideas from reactive programming, peer-to-peer distribution, and large-scale modelling. The objective of this contribution is to enable efficient analytics over dis- tributed datasets of frequently changing data.
This contribution is based on the work that has been presented in the following paper:
• Thomas Hartmann, Assaad Moawad, Fran¸cois Fouquet, Gr´egory Nain, Jacques Klein, and Yves Le Traon. Stream my models: Reactive peer-to-peer distributed mod- [email protected]. In 18th ACM/IEEE International Conference on Model Driven En- gineering Languages and Systems, MoDELS 2015, Ottawa, ON, Canada, September 30 - October 2, 2015, pages 80–89, 2015
Weaving machine learning into domain modelling. The fourth and last con- tribution of this thesis addresses the challenge of modelling and combining domain data and knowledge together with machine learning. It defines so-called micro learn- ing units, which decompose learning tasks into reusable, chainable, and independently computable units. The concept presented in this approach extends data models with the ability to represent learned knowledge on the same level as domain data. This contribution aims to weave micro machine learning into data modelling, i.e., to allow to model learning and domain knowledge in the same data models and with the same concepts.
Part I: Background and state of the art Part II: Analysing data in motion and what-if analysis Part III: Reasoning over distributed data and combining domain knowledge with machine learning
Part IV: Industrial application and conclusion
- A temporal context model, time-relative navigation, temporal data storage. - A multi-dimensional graph data model, index structures and storage mechanisms - A transparent peer-to-peer distribution and stream processing model
- Reusable, chainable, and independently computable micro machine learning units
Thesis contribution: enabling model-driven live analytics for CPSs Part III
Part II
Data modelling, data analytics, database
technologies, and machine learning Part I
Industrial application on a smart grid system, conclusion, discussion, and future work Part IV
00101011 11001011 10110011
Figure 1.7: Thesis structure
• under submission at International Journal on Software and Systems Modeling (SoSyM): Thomas Hartmann, Assaad Moawad, Francois Fouquet, and Yves Le Traon. The Next Evolution of MDE: A Seamless Integration of Machine Learning into Domain Modeling • Thomas Hartmann, Assaad Moawad, Fran¸cois Fouquet, Yves Reckinger, Tejeddine Mouelhi, Jacques Klein, and Yves Le Traon. Suspicious electric consumption detection based on multi-profiling using live machine learning. In 2015 IEEE International Conference on Smart Grid Communications, SmartGridComm 2015, Miami, USA, November 2-5, 2015