[Lisa_seminaires] Fwd: Colloque DIRO, *lundi* 30 janvier, François Fouquet (Luxembourg)

Yoshua Bengio yoshua.umontreal at gmail.com
Jeu 26 Jan 23:05:32 EST 2017


Another job talk with some machine learning in it, more, hopefully.

---------- Forwarded message ----------
From: Pierre McKenzie <mckenzie at iro.umontreal.ca>
Date: 2017-01-26 18:54 GMT-05:00
Subject: Colloque DIRO, *lundi* 30 janvier, François Fouquet (Luxembourg)
To: seminaires at iro.umontreal.ca


*Enabling Model-Driven Analytics for Cyber Physical systems *

par



* François Fouquet *

 University of Luxembourg

*Lundi 30 janvier, 15:30-16:30*, *Salle 3195*, Pavillon André-Aisenstadt

    Université de Montréal, 2920 Chemin de la Tour

Café avant 15:00-15:30

*Cette présentation sera donnée en français.*



*Résumé:*

Advances in software, embedded computing, sensors, and networking
technologies will lead to a new generation of smart cyber-physical systems.
Such systems have to continuously monitor and collect data from physical
processes in order to make decisions fast, i.e., in live. However, the
complex nature combined with the massive amount of data generated by such
systems impose fundamental challenges. Smart grids are excellent
illustrations of such systems. They combine novel country-wide
communication medias together with many energy sensors to enable the next
generation of sustainable infrastructure, including renewable energies. For
instance, on-field grid operators can now leverage these
newly collected data to empower live simulation tools that allow them to
foresee the impacts of maintenance actions before their actual application
on the real grid. However, developing such software systems, at the
crossroad of big data, modelling and simulation is  very challenging.
In collaboration with the main electricity grid operator in Luxembourg, we
have developed a novel analytic approach to process such complex data in
live.

During this talk, I will present what we call model-driven live analytics.
I will focus on the key enablers to make this approach scalable to the size
of country-wide CPSs. The main contribution of this work is a
multi-dimensional graph data model that brings raw data, domain knowledge,
and machine learning together in a single model, which can drive live
analytic processes. Firstly, data handled by cyber-physical systems is
usually dynamic and changes frequently and at different paces. I will
present a temporal graph data model and storage system, which consider time
as a first-class property and allow to analyse frequently changing data.
Additionally, I will present how a continuous sequence of sensor values can
be efficiently encoded using live mathematical model inference.
Secondly, making sustainable decisions requires to anticipate which impacts
certain actions could have. In some cases, hundreds or thousands of such
hypothetical actions must be explored ahead before any solid decision can
be taken. I will present our approach to deal with such need - a
multi-dimensional model that efficiently represent, store and analyse many
different alternatives of the same system in live. Thirdly, to make smart
decisions, cyber-physical systems must continuously refine behavioural
models that are known at design time, with what can only be learned from
live data. During this talk, I will present how we have combined machine
learning and the multi-dimensional graph data model to empower live
analytics for cyber-physical systems. Finally, I will conclude this talk
with details about the open source project, which is developed around these
research ideas, and share the lessons learned with respect to
high-performance Java and big data.
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