European IoT Analytics Summit
Paris, 24 November 2016
Program

Introduction

The European IoT Analytics Summit is a meeting place for both academia, government and industr y to discuss upcoming challenges of IoT data mining, machine learning and data science methods.

This event is organized and funded by the Big Data and Market Insights Chair at Institute Mines-Telecom.

The Internet of Things (IoT), the large network of physical devices that extends beyond the typical computer networks, will be creating a huge quantity of data in real time in the next future. The realization of IoT depends on being able to gain the insights hidden in the vast and growing seas of data available. Since current approaches don't scale to Internet of Things (IoT) volumes, new systems with novel mining techniques are necessary due to the velocity, but also variety, and variability, of such data. There is a great deal of interest in analyzing data from IoT.

The main goal for this year's event is to bring together top researchers from academia, as well as top data scientists from industry and government with the special focus of evolving infrastructures for IoT.

EuroIoTA 2016 will take place in Paris on November 24th.

The topics of interest include, but are not limited to:

  • IoT Data Analytics,
  • Data Mining for IoT
  • Machine Learning for IoT
  • IoT & Data Stream Mining
  • Infrastructures for IoT
  • Communication Protocols
  • IoT for manufacturing
  • IoT Data Analytics applications
    • smart cities
    • healthcare
    • energy
    • environment
    • intelligent transportation systems
    • green mobility

Save the Date: November 25th

The Internet of Things in the European Ecosystem

An international conference on the legal, economic and social aspects of the Internet of Things, organised by the Chair Values And Policies of Personal Information.

Program

8:00 Registration


8:45-9:00 Welcome Talk by Yves Poilane, Director of Télécom ParisTech


9:00-10:00 Keynote Talk: Michael May, Siemens Corporate Technology, Towards Industrial Machine Intelligence

May The next decade will see a deep transformation of industrial applications by big data analytics, machine learning and the internet of things. Industrial applications have a number of unique features, setting them apart from other domains. Central for many industrial applications in the internet of things is time series data generated by often hundreds or thousands of sensors at a high rate, e.g. by a turbine or a smart grid. In a first wave of applications this data is centrally collected and analyzed in Map-Reduce or streaming systems for condition monitoring, root cause analysis, or predictive maintenance. The next step is to shift from centralized analysis to distributed in-field or in situ analytics, e.g in smart cities or smart grids. The final step will be a distributed, partially autonomous decision making and learning in massively distributed environments.

In this talk I will give an overview on Siemens’ journey through this transformation, highlight early successes, products and prototypes and point out future challenges on the way towards machine intelligence. I will also discuss architectural challenges for such systems from a Big Data point of view.

Biography: Michael May is Head of the Technology Field Business Analytics & Monitoring at Siemens Corporate Technology, Munich, and responsible for eleven research groups in Europe, US, and Asia. Michael is driving research at Siemens in data analytics, machine learning and big data architectures. In the last two years he was responsible for creating the Sinalytics platform for Big Data applications across Siemens’ business.

Before joining Siemens in 2013, Michael was Head of the Knowledge Discovery Department at the Fraunhofer Institute for Intelligent Analysis and Information Systems in Bonn, Germany. In cooperation with industry he developed Big Data Analytics applications in sectors ranging from telecommunication, automotive, and retail to finance and advertising.

Between 2002 and 2009 Michael coordinated two Europe-wide Data Mining Research Networks (KDNet, KDubiq). He was local chair of ICML 2005, ILP 2005 and program chair of the ECML/PKDD Industrial Track 2015. Michael did his PhD on machine discovery of causal relationships at the Graduate Programme for Cognitive Science at the University of Hamburg.


10:00-10:30 Coffee break


10:30-11:10 Georges Hébrail, EDF R&D, Some Examples of Sensor Data Analytics in a Large Electric Power Company


11:10-11:50 Igor Carron, LightOn, Sketching Data Streams at the Speed of Light


11:50-12:30 Keun-Woo Lim, Telecom ParisTech: Variable neighborhood Prediction of temporal collective profile


12:30-14:00 Lunch


14:00-15:00 Keynote talk: Merouane DEBBAH, Huawei, 5G: a revolution or an evolution for IoT

Debbah Targeted for 2020, 5G will take the eco-system to a whole new level. New applications, new business models and even new industries will spring up around 5G. With 5G, we can expect our high speed connections to extend into our vehicles, into our things, with fewer interruptions and new ways of working or remote working starting to take off. If we look at some of the goals of 5G versus where we are today, we can see the gap that has to be bridged over the next few years. The goal of 1ms latency is nearly 50x better than current LTE systems. In order to go from 100Mbps per user to 10Gbps, we need 100x the throughput per connection. The current 10,000 connections per square kilometer needs to increase to 1Million connections which is a 100x increase in density. Reliable communications today with LTE top can sustain 350km/h and we expect to bring that up by 1.5x to 500km/h . Finally the current core networks and backhaul/front-haul are inflexible with wasted pools of bandwidth. The introduction of SDN/NFV will allow much better ability to chop up and virtualize the network resources for lower operational costs and capital costs and much greater flexibility. In this talk, we will briefly describe the candidate 5G technologies and provide a look at the actual worldwide standardization process.

Biography: Mérouane Debbah entered the Ecole Normale Supérieure de Cachan (France) in 1996 where he received his M.Sc and Ph.D. degrees respectively. He worked for Motorola Labs (Saclay, France) from 1999-2002 and the Vienna Research Center for Telecommunications (Vienna, Austria) until 2003. From 2003 to 2007, he joined the Mobile Communications department of the Institut Eurecom (Sophia Antipolis, France) as an Assistant Professor. Since 2007, he is a Full Professor at CentraleSupelec (Gif-sur-Yvette, France). From 2007 to 2014, he was the director of the Alcatel-Lucent Chair on Flexible Radio. Since 2014, he is Vice-President of the Huawei France R&D center and director of the Mathematical and Algorithmic Sciences Lab. Mérouane Debbah is a recipient of the ERC grant MORE (Advanced Mathematical Tools for Complex Network Engineering). He is a IEEE Fellow and a WWRF Fellow. During his career, he received more than 16 awards, among the IEEE Glavieux Prize Award.


15:00-15:30 Coffee Break


15:30-16:10 Madhusudan Giyyarpuram, Orange, Data analytics for monitoring IoT infrastructures


16:10-16:50 Jan van Rijn, University of Freiburg, OpenML.org: Networked Science and IoT Data Streams


16:50-17:40 Tian Guo, EPFL, Hybrid Neural Networks for Time Series Learning


Registration

Registration is free but it is mandatory. Please register using this form.

Organization

  • Albert Bifet (Chair, Telecom ParisTech)
  • Michele Berlingerio (IBM)
  • Talel Abdessalem (Telecom ParisTech)
  • Pascal Thubert (Cisco)
  • Joao Gama (University of Porto)

Sponsors

Location

Address

Télécom ParisTech
46 rue Barrault
75013 Paris
France

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