Les "systèmes intelligents" constituent la nouvelle génération de systèmes embarqués, qui, en s'appuyant sur les caractéristiques de robustesse et de déterminisme de leurs aînés, se connectent au cloud afin d'enrichir l'expérience utilisateur, qu'il s'agisse d'entreprises (collectant des données ou surveillant des systèmes par exemple), de particuliers (à la maison ou dans un contexte médical, ou bien dans la voiture) ou bien d'autres machines (dans le cas de systèmes automatisés à grande échelle). Le cloud et particulièrement Windows Azure fourni les vecteurs de communication et les moyens de stocker massivement des données et de les traiter, déchargeant ainsi les installations locales et donc rendant le déploiement de ses systèmes plus simple. Cette session, riche en exemples concrets, présentera la stratégie qui est celle de Microsoft autour du futur des systèmes embarqués, et leur connexion au cloud, ainsi que les technologies et les partenariats mis en oeuvre pour accélérer ces déploiements de systèmes intelligents. avec un exemple qui parlera à tous: le futur de la voiture, avec Windows Embedded Automotive!

1. palais des
congrès
Paris
7, 8 et 9
février 2012

2. Utilisation du Cloud
dans les Systèmes
Intelligents
Mardi 7 février
Charlie Grabiaud
Partner Technology Manager
Windows Embedded, Microsoft

3. Agenda

4. Market opportunity
Billions of systems
$1.2
Trillion
WW market
10.0 By 2015
$520
8.0 Billion
6.0 Today
4.0
2.0
0.0
Traditional Embedded Intelligent Systems
IDC, 2011

6. Stages of Intelligent Systems
Stage A:
Stage B: Stage C: Stage D:
Discrete Technology
Connected System Managed System Analytical System
Solutions
System for
a specific business Data and information is shared between two or more systems
purpose
Limited automatic Connected devices allow data to be automatically updated in
data-flow between
devices and back end back end systems
Two-way connectivity allows for
remote management of devices
System capable of
analytics and BI

7. Microsoft Confidential
Data is the New Currency
Connectivity
3
4

8. Device Systems Analytics
Heartbeat (On, Off)
Transactions
Performance
Logistics
Efficiency
Records
Productivity
Events
Telemetry
Health and System
Performance Interaction
Data Data
System Contextual
Related Data Data
CRM (Customer) Weather
ERP (Inventory, Employee) Traffic
Market Intelligence GPS
Fraud/Theft Detection Maps

9. Today’s Data Challenges
Too slow Too much
Results have outlived their value Vast amount of data, little information
Too costly Too little
High integration costs, Missing the right data
Complex toolsets
Too vague
No context to the data

10. Data Generation vs Capacity
Data generation
Data hardware
capabilities
Processing all the data centrally in premises
becomes
either a bottleneck or too costly:
• Must bring some of the processing
closer to the data source
• Must use public cloud scaling
Bandwidth / Server
capacity
2010 2015 2020

11. Benefits of Public Cloud
Computing
• Data & services accessible from anywhere
– Office, Home and on the road
• Almost unlimited resources
– Internet-scale computing and services platform
• Very high availability
– Automatic data redundancy and distribution
– Robustness of Microsoft's datacenters and Windows Azure
• Cost optimization
– No huge CAPEX before development can start
– Pay Per Use Model
– Good Windows Azure Applications are scalable by definition

12. Microsoft Intelligent Systems
support
Windows Embedded Compact
Windows Embedded Standard
Windows Embedded Enterprise, …
Windows Azure
Windows Embedded Server
Windows Embedded Storage Server, …
Microsoft SQL
Microsoft Dynamics
Microsoft Sharepoint, ...

13. Windows Embedded+Azure Scenarios
• Industrial Automation
• Automotive
• Public Services
• Energy
• Medical

14. Industrial cloud services
• Storage of auditable data
– Small and mid size companies without own DC
– Long-term backup and availability
• Device Monitoring and remote maintenance
– Machines and equipment in remote locations
• Web based engineering
– More computing power for compiling
– Team engineering across multiple locations

15. Siemens/Intel/Microsoft POC
• Cooperation of
– Siemens
– Intel
– Microsoft
• Data in
– SQL Azure
• Services on
– Windows Azure
• Siemens Devices
– Windows Embedded
– Intel CPUs

16. Global Infotainment Trends
Today 2015
Device Types Market Share End User Cost Market Share End User Cost
Video/3D Nav/ <10% $3000 20% $2000
Online Services
Color Screen/Speech
30% $1000 50% < $1000
UI/Navigation
USB/BT Telephone/ Standard or
40% < $500 70%
Media $250
Radio/CD/ 100% Standard (Free) 100% Standard (Free)
MP3 Playback
60 Million Cars/Light Trucks 80 Million Cars/Light Trucks

17. The Automotive Design Lifecycle
Today  Long Lead Times and Fixed Functionality
3-5 yr 1-2 yr 2+ years 7 years 10-15 years…
Emerging  Faster Development Cycles, Annual Releases, Continuous New Functionality
3-4
3-4 Mo 9 Mo 7 years 10-15 years…
Mo

18. Daimler Project: eMobility
• Enables drivers remote
access to vehicle information
• Monitor charging state and
possible range
• Combine car data with other
information
• Access data at any time from
every device

19. eMobility: Visualize data
• Use Bing routing service to
calculate possible range
• Combine additional
information and charging
spot location for exact
calculation
• Increase confidence in
vehicle possibilities

20. Giletta, Italy : Intelligent Salt
Spreading
Situation
• Fleet of Trucks spreading salt on the road when snowing
• Truck drivers control the spreading manually, using
predefined route
Intelligent System solution
Objectives
- Spreading performance •On-board navigation
and cost and control system
- Safety on the road
Challenges - Environmental impact •Back-end system
• Salt is expensive - Better alignment to aggregating and
• Unnecessary pollution created by trucks weather conditions. computing data
• Slow in some areas – no dynamic system to chose
spreading location
• System not effective
Spreading Parameters
Maps
view
iMx27 with
WinCE6 R3
Dedicated CAN Bus
Snowplough
Controls

21. Technology Enablers
• Windows Embedded
To power the on-board
controller (ARM, Real
Time & connectivity)
• Windows Azure
Cloud-based application
to analyze data and
enable decision Creation of Additional
business value
• Hundreds of tons of salt saved
• Improved security on the road
• Reduced maintenance costs
• Reduced environmental impact
• Planned extension to other
New Usage Scenario services (transport or recycling)
• Provide accurate
directions to the driver
• New Data collection of
highway infrastructures
and
services, weather, truck
location data and
traffic data

22. Home Energy Gateway
Architecture

23. Home Energy Gateway
Architecture
Integration & Analytics:
• Base Services (ex. Authentication)
• Backoffice system integration
• Services Directory (reusability)
• Business Analytics
Internet Portal:
• Secure Access
• Customizable Content
• Services Catalog
• Services & Product Search
• Client Data
Back-End Systems
Home
PLC
Home Energy Mobile, PC, TV, Other
Smart Meters Gateway
Multi Channel and Multi Device

24. Towards Intelligent Medical
Systems
 Health drivers
–Aging population
–Increasing costs
–Prevalence of chronic disease
–Consumer expectations of service quality and life style
continuity
–Significant and accelerating staffing shortages
 Health Intelligent Systems
–Intelligent/connected medical devices
(glucometers, blood pressure monitors)
–Electronic medical record (EMR)/personal health record
(PHR) systems
–Care management systems (enables remote care by
clinicians)
–Telemedicine and remote patient monitoring
–Telepresence/video conferencing
–Patient portals

25. Medical Proof Of Concept

26. HealthVault in Medical POC

27. Analytics from Edge to Cloud

28. Understanding Streaming Data
(1)
Question: “how many red cars are in the parking lot”.
Answering with a relational database:
• Walk out to the parking lot.
• Count vehicles that are
Red
Cars
SELECT COUNT(*) FROM ParkingLot
WHERE type = ‘AUTO’
AND color = ‘RED’

29. Understanding Streaming Data
(2)
What about: “How many red cars
took the I-80 interchange to San
Francisco in the last hour”?
Answering with a relational database:
• Pull over and park all vehicles in a lot,
keeping them there until the end of the
hour.
• At the end of the hour, count vehicles that Doesn’t seem like a
are in the lot. great solution…
• Then deliver the answer

30. Understanding Streaming Data
(3)
Different kinds of questions require different ways of answering them.
The last questions we looked at are best answered with a stream
data processing engine, or complex event processing engine.
How would a streaming engine do the processing for this scenario?
• Stand by the freeway, count red cars as they pass by.
• Keep updating the answer internally, keep delivering the answer as needed
by the consumers.
This is the streaming data paradigm in a nutshell –
ask questions about data in flight.

31. Event-Driven Applications
Analytical results need to reflect important changes in business reality
immediately and enable responses to them with minimal latency
Database-driven Applications Event-driven Applications
Query Paradigm Ad-hoc queries or requests Continuous standing queries
Latency Seconds, hours, days Milliseconds or less
Data Rate Hundreds of events/sec Tens of thousands of events/sec
or more
Query Semantics Declarative relational analytics Declarative relational and
temporal analytics
request
Event output
stream
input
stream
response

32. Example: Microsoft Campus
Shuttle Bus Tracking
• Plot current position for
Redmond campus
shuttles
• Track specific shuttles
• Identify when shuttles
approach specific
destinations
• Proximity queries with
SQL Spatial Libraries

33. Scenarios for Event-Driven
Applications
Latency
Months
Days
Relational Database Applications
Hours Operational Analytics
Applications, e.g., Logistics, etc.
Minutes Data Warehousing
Applications
Web Analytics Applications
Seconds
100 ms Monitoring
Applications Manufacturing Applications Financial trading
Applications
< 1ms
0 10 100 1000 10000 100000 ~1million
Aggregate Data Rate (Events/sec.)

34. StreamInsight™
Rich Analytics • Continuous processing of event streams from
multiple sources
• Based on rich declarative query language
• Optimized for analytics over time-series data
Intelligent Processing • Express and detect complex pattern and device
profiles
• Push richer analytics down to the device (pattern
redeployment)
Unified Experience • Provide uniform semantics & development
experience from server to the edge
• Seamlessly transition between historical and real-
time data
Microsoft Optimize data traffic • Send only relevant information from device
StreamInsight™ • Eliminate bottleneck at the mid-tier

35. StreamInsight™ Platform
StreamInsight™
Application Development
Event sources StreamInsight™ Application at Runtime Event targets
Input Output
Devices, Sensors Adapters StreamInsight™ Engine Adapters Pagers &
Monitoring devices
Standing Queries KPI Dashboards,
`
SharePoint UI
Web servers
Query Query
Logic Logic
Trading stations
Event stores & Databases
Query
Logic
Stock ticker, news feeds Event stores & Databases

36. Analytics Platform
Assets
Process Sensors
& Control
SI SI
Auto
Robots
SI SI
Global, cross-asset analytics for
SI aggregation and correlation of in-flight SI SI
events; analytics on historical data
Cross-asset Analytics &
Per-asset analytics for lightweight Mining
SI processing and filtering, computed
close to the asset
Hosted in the
Embedded in the asset Integrated with .NET
cloud/on-premise
• Creates adaptable, • Gather insight from large • Extensible to incorporate
network friendly, remotely collections of assets domain specific analytic
manageable assets • Mine historical data to needs
create/validate new models • Rich development tools to
reduce total cost of
ownership

37. Connected Car Scenario
Exploratory analysis of
historical data across cars to
identify problems or enhance
driver experience
OEM Engr.
New Analytics
models, updates, etc. Computation
for deployment
Significant Operational Data
(Battery level, engine status,
Recommendations speed etc.)
(Route, recharging
station, business location, etc.) Location Data
(GPS coordinates)
Servicing/ Diagnostics
(Service Contextual Data
recommendation, Updates, etc (Destination, address, etc.)
.)
Asset
Analytic
Car Operation High Customer Satisfaction

38. World of Windows Embedded

39. Pour aller plus loin
Prochaines sessions des Dev Camps
Chaque semaine, les 10
Live Open Data - Développer des applications riches avec le
février
DevCamps 2012
16
Meeting protocole Open Data
ALM, Azure, Windows Phone, HTML5, OpenData février
Live
Meeting
Azure series - Développer des applications sociales sur
la plateforme Windows Azure
2012
http://msdn.microsoft.com/fr-fr/devcamp
17
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février
Meeting three.js
2012
Téléchargement, ressources 21
février
Live La production automatisée de code avec CodeFluent
Meeting Entities
et toolkits : RdV sur MSDN 2012
2 mars Live Comprendre et mettre en oeuvre le toolkit Azure pour
http://msdn.microsoft.com/fr-fr/ 2012 Meeting Windows Phone 7, iOS et Android
6 mars Live
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2012 Meeting
Les offres à connaître 9 mars
2012
Live
Meeting
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90 jours d’essai gratuit de Windows 13 mars
2012
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Meeting
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Azure 14 mars Live TFS Health Check - vérifier la bonne santé de votre
www.windowsazure.fr 2012 Meeting plateforme de développement
15 mars Live Azure series - Développer pour les téléphones, les
2012 Meeting tablettes et le cloud avec Visual Studio 2010
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