A Native Versioning Concept to Support Historized Models at Runtime

Hartmann, Thomas; Fouquet, Francois; Nain,; Morin, Brice; Klein, Jacques; Barais, Olivier; Traon, Yves Le

doi:10.1007/978-3-319-11653-2_16

Cited by 22 publications

(24 citation statements)

References 15 publications

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“…Different approaches to represent and traverse temporal data have been suggested, e.g., [18], [42]. Regardless of the concrete implementation (in the implementation of our framework we follow the approach presented in [21], [20]), for this paper we assume that our object graphs evolve over time and that we can access historical data.…”

Section: A Modeling Techniquesmentioning

confidence: 99%

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

Hartmann

Moawad²,

Fouquet

et al. 2017

Softw Syst Model

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Abstract-Machine learning algorithms are designed to resolve unknown behaviours by extracting commonalities over massive datasets. Unfortunately, learning such global behaviours 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 behaviour on a per-element basis and compose these small learning units together. However, combining and composing learned behaviours from different elements is challenging and requires domain knowledge. Therefore, there is a need to structure and combine the learned behaviours 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 micro learning units. These micro learning 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 behaviour while the performance is fast enough to be used for live learning.

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Section: A Modeling Techniquesmentioning

confidence: 99%

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

Hartmann

Moawad²,

Fouquet

et al. 2017

Softw Syst Model

Self Cite

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“…To tackle these problems, we proposed in this conference last year [14], a native versioning concept for each model elements. In this past contribution, we defined that every model elements can exist in a sequence of versions, attached to its unique identifier (e.g., path).…”

Section: B Native Independent Versioning For Context Elementsmentioning

confidence: 99%

“…2. A smartphone meta model work, and in order to enable independent model elements versioning [14], we have extended the definition of an object to the following: An object contains an index tree and a set of segments, each segment contains a set of attributes and a set of relationships. This definition is based on a discrete strategy which defines a new segment or version in case of modification of any attribute of the object.…”

Section: A Continuous and Efficient Model For Iotmentioning

confidence: 99%

“…In addition, we introduce the KPI continuity reconstruction ability (CRA), which aims to evaluate the ability of the modeling layer to rebuild a continuous signal -even in case of sampling rate variation of one model element. To conduct these experiments, we reused our past work [14], which has been demonstrated to be highly scalable following a discrete strategy. We enhanced this implementation with the continuous polynomial based strategy, and evaluated it on various IoT data sets, with and without this new optimization.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…In a previous work [14], we introduced a modeling technique, which natively integrates versioning on a model element level. This allows to model a context in which every element can evolve independently.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Beyond discrete modeling: A continuous and efficient model for IoT

Moawad

Hartmann

Fouquet

et al. 2015

2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS)

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Abstract-Internet of Things applications analyze our past habits through sensor measures to anticipate future trends. To yield accurate predictions, intelligent systems not only rely on single numerical values, but also on structured models aggregated from different sensors. Computation theory, based on the discretization of observable data into timed events, can easily lead to millions of values. Time series and similar database structures can efficiently index the mere data, but quickly reach computation and storage limits when it comes to structuring and processing IoT data. We propose a concept of continuous models that can handle high-volatile IoT data by defining a new type of meta attribute, which represents the continuous nature of IoT data. On top of traditional discrete object-oriented modeling APIs, we enable models to represent very large sequences of sensor values by using mathematical polynomials. We show on various IoT datasets that this significantly improves storage and reasoning efficiency.

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