palais des congrès Paris

7, 8 et 9 février 2012

Mardi 7 févrierCharlie GrabiaudPartner Technology ManagerWindows Embedded, Microsoft

Utilisation du Cloud dans les Systèmes Intelligents

Analytics from Edge to Cloud

Windows Embedded+Azure scenarios

Evolution of Embedded Devices

Agenda

Market opportunity

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 20150.0

2.0

4.0

6.0

8.0

10.0

Billions of systems

Traditional Embedded Intelligent Systems

$520 BillionToday

$1.2 TrillionBy 2015

WW market

Today800 Million

unitsper year

20152.3 Billion units

per year

IDC, 2011

Intelligent Systems

Identity

Security

Connectivity

Manageability

User experience

Analytics

Stage B:Connected System

Stage C:Managed System

Stage D:Analytical System

Stage A:Discrete Technology

Solutions

System for a specific business

purpose

Limited automatic data-flow between

devices and back end

Data and information is shared between two or more systems

Connected devices allow data to be automatically updated in back end systems

Two-way connectivity allows for remote management of devices

System capable of analytics and BI

Stages of Intelligent Systems

Microsoft Confidential

EmbeddedDevice

1

Connectivity2EnterpriseBack-end

3

45

Data is the New Currency

Device Systems Analytics

Heartbeat (On, Off)Performance

EfficiencyProductivityTelemetry

Health and Performance

Data

CRM (Customer)ERP (Inventory, Employee)Market IntelligenceFraud/Theft Detection

System Related Data

TransactionsLogisticsRecordsEvents

System Interaction

Data

WeatherTrafficGPSMaps

Contextual Data

Too slowResults have outlived their value

Too vagueNo context to the data

Too muchVast amount of data, little information

Too littleMissing the right data

Too costlyHigh integration costs,

Complex toolsets

Today’s Data Challenges

$

Data Generation vs Capacity

2010 2015 2020

Data generation

Data hardwarecapabilities

Bandwidth / Servercapacity

Processing all the data centrally in premises becomeseither a bottleneck or too costly:• Must bring some of the processing

closer to the data source• Must use public cloud scaling

Benefits of Public Cloud Computing

Data & services accessible from anywhereOffice, Home and on the road

Almost unlimited resourcesInternet-scale computing and services platform

Very high availabilityAutomatic data redundancy and distributionRobustness of Microsoft's datacenters and Windows Azure

Cost optimizationNo huge CAPEX before development can startPay Per Use Model Good Windows Azure Applications are scalable by definition

Microsoft Intelligent Systems support

Business Intelligence

Network

Devices

Windows Embedded CompactWindows Embedded StandardWindows Embedded Enterprise, …

Windows AzureWindows Embedded ServerWindows Embedded Storage Server, …

Microsoft SQLMicrosoft DynamicsMicrosoft Sharepoint, ...

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

Industrial cloud services

Storage of auditable dataSmall and mid size companies without own DCLong-term backup and availability

Device Monitoring and remote maintenanceMachines and equipment in remote locations

Web based engineeringMore computing power for compilingTeam engineering across multiple locations

Siemens/Intel/Microsoft POC

Cooperation ofSiemensIntelMicrosoft

Data in SQL Azure

Services onWindows Azure

Siemens DevicesWindows EmbeddedIntel CPUs

Today

60 Million Cars/Light Trucks

<10%

100%

Market Share

$3000

Standard (Free)

End User Cost

2015

80 Million Cars/Light Trucks

100%

Market Share

Standard (Free)

End User Cost

Global Infotainment Trends

Device Types

Video/3D Nav/Online Services

Color Screen/Speech UI/Navigation

USB/BT Telephone/Media

Radio/CD/MP3 Playback

20%

50%

70%

$2000

< $1000

Standard or $250

30%

40%

$1000

< $500

20%

50%

70%

$2000

< $1000

Standard or $250

30%

40%

$1000

< $500

The Automotive Design Lifecycle

Today Long Lead Times and Fixed Functionality

Standard Practice

Emerging Faster Development Cycles, Annual Releases, Continuous New Functionality

New Systems Model

Supplier / Platform Selection & Dev. Process

Production

7 years

Maintenance/Support

10-15 years…

Research

3-5 yr

Evaluation

1-2 yr

Development

2+ years

SOP

Platform Development Process

Production and Annual Releases

7 years

Maintenance/Support

10-15 years…

Research

3-4 Mo

Evaluation

3-4 Mo

Development

9 Mo

V2 V3 V4 V5 V6 V7SOP

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

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

Giletta, Italy : Intelligent Salt Spreading

Objectives- Spreading performance and cost- Safety on the road- Environmental impact- Better alignment to weather conditions.

Situation• F

leet of Trucks spreading salt on the road when snowing

• Truck drivers control the spreading manually, using predefined route

Challenges• S

alt is expensive

• Unnecessary pollution created by trucks

• Slow in some areas – no dynamic system to chose spreading location

• System not effective

Maps view

Spreading Parameters

SnowploughControls

Dedicated CAN Bus

iMx27 withWinCE6 R3

Intelligent System solution•On-board navigationand control system

•Back-end systemaggregating andcomputing data

Technology Enabler

s• W

indows Embedded To power the on-board controller (ARM, Real Time & connectivity)

• Windows Azure

Cloud-based application to analyze data and enable decision

New

Usage Scenari

o• P

rovide accurate directions to the driver

• New Data collection of highway infrastructures and services, weather, truck location data and traffic data

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 services (transport or recycling)

Home Energy Gateway Architecture

Internet Portal:• Secure Access• Customizable Content• Services Catalog• Services & Product Search• Client Data

Multi Channel and Multi DeviceMobile, PC, TV, Other

Home

PLC

Integration & Analytics:• Base Services (ex. Authentication)• Backoffice system integration• Services Directory (reusability)• Business Analytics

Home Energy Gateway

Back-End Systems

Home Energy Gateway Architecture

Smart Meters

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

Medical Proof Of Concept

HealthVault in Medical POC

Analytics from Edge to Cloud

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 ParkingLotWHERE type = ‘AUTO’AND color = ‘RED’

Understanding Streaming Data (2)

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

are in the lot.• Then deliver the answer

Doesn’t seem like a great solution…

What about: “How many red cars took the I-80 interchange to San Francisco in the last hour”?

Understanding Streaming Data (3)

Different kinds of questions require different ways of answering them.

This is the streaming data paradigm in a nutshell – ask questions about data in flight.

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.

Event-Driven Applications

Analytical results need to reflect important changes in business reality immediately and enable responses to them with minimal latency

Query Paradigm

Latency

Data Rate

Query Semantics

Database-driven Applications

Ad-hoc queries or requests

Seconds, hours, days

Hundreds of events/sec

Declarative relational analytics

Event-driven Applications

Continuous standing queries

Milliseconds or less

Tens of thousands of events/sec or more

Declarative relational and temporal analytics

request

response

Event output stream

input stream

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

Scenarios for Event-Driven Applications

Relational Database Applications

Financial trading Applications

Latency

0 10 100 1000 10000 100000 ~1million

Months

Days

Hours

Minutes

Seconds

100 ms

< 1ms

Operational Analytics Applications, e.g., Logistics, etc.

Manufacturing ApplicationsMonitoring Applications

Data Warehousing Applications

Web Analytics Applications

Aggregate Data Rate (Events/sec.)

StreamInsight™

MicrosoftStreamInsight™

Rich Analytics

Intelligent Processing

Unified Experience

Optimize data traffic

• Continuous processing of event streams from multiple sources

• Based on rich declarative query language• Optimized for analytics over time-series data

• Express and detect complex pattern and device profiles

• Push richer analytics down to the device (pattern redeployment)

• Provide uniform semantics & development experience from server to the edge

• Seamlessly transition between historical and real-time data

• Send only relevant information from device• Eliminate bottleneck at the mid-tier

StreamInsight™Application Development

StreamInsight™ Platform

Event targets

`

Event stores & Databases

Pagers &Monitoring devices

KPI Dashboards, SharePoint UI

Trading stations

Event sources

Devices, Sensors

Web servers

Event stores & Databases

Stock ticker, news feeds

Standing Queries

Query Logic

Query Logic

Query Logic

InputAdapters

OutputAdaptersStreamInsight™ Engine

StreamInsight™ Application at Runtime

Analytics Platform

Hosted in the cloud/on-premise

• Gather insight from large collections of assets

• Mine historical data to create/validate new models

Embedded in the asset

• Creates adaptable, network friendly, remotely manageable assets

Integrated with .NET

• Extensible to incorporate domain specific analytic needs

• Rich development tools to reduce total cost of ownership

Global, cross-asset analytics for aggregation and correlation of in-flight events; analytics on historical data

SI

Per-asset analytics for lightweight processing and filtering, computed close to the asset

SI

Assets

Cross-asset Analytics & Mining

SI SI

Robots

SI

Sensors

SI

Process & Control

SI Auto

SI

OEM Engr.

Connected Car Scenario

Analytics Computation

AssetAnalytic

High Customer SatisfactionCar Operation

New models, updates, etc. for deployment

Recommendations(Route, recharging station, business location, etc.)

Servicing/ Diagnostics(Service recommendation, Updates, etc.)

Significant Operational Data (Battery level, engine status, speed etc.)

Location Data (GPS coordinates)

Contextual Data(Destination, address, etc.)

Exploratory analysis of historical data across cars to

identify problems or enhance driver experience

World of Windows Embedded

Chaque semaine, les DevCampsALM, Azure, Windows Phone, HTML5, OpenDatahttp://msdn.microsoft.com/fr-fr/devcamp

Téléchargement, ressources et toolkits : RdV sur MSDNhttp://msdn.microsoft.com/fr-fr/

Les offres à connaître90 jours d’essai gratuit de Windows Azure www.windowsazure.fr

Jusqu’à 35% de réduction sur Visual Studio Pro, avec l’abonnement MSDN www.visualstudio.fr

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