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IoT and Big Data
Internet of Things and Big Data
Part I / III: Vision and Concrete Use Cases
IoT and Big Data
Joint Webinar Series:
Register to Reserve your Seat
Webinar 1: Vision & Use Cases
Webinar 2: Data Management Requirements for the IoT
Webinar 3: From Concept to Code
IoT and Big Data
Today’s Speakers
Dirk Slama, Director of Business Development
Bosch Software Innovations
20+ years of experience in large-scale distributed application
projects including M2M and IoT projects
Emil Berthelsen, Principal Analyst
Machina Research
Two decades of experience with management, strategic and research
consulting in business process management, telecoms and IT and more
recently focused on M2M, IoT and Big Data
Mat Keep, Product Management & Marketing
MongoDB
15 years working in high scale, data driven systems, working with
relational databases before moving into NoSQL and Big Data
platforms
IoT and Big Data
"Fixed" computing Mobility/BYOD Internet of things Internet of everything
Source: Cisco IBSG, 2013
(you go to the device) (the device goes with you) (age of devices) (people, process, data, things)
1995 2000 2013 2020
200M
10B
50B
Rapid growth of connected things
IoT and Big Data
IoT Predictions (by 2020-22)
7,1tn IoT Solutions Revenue | IDC
Some Big Numbers:
1,9tn IoT Economic Value Add | Gartner
309bn IoT Supplier Revenue | Gartner
50bn Connected Devices | Cisco
14bn Connected Devices | Bosch SI
Some small numbers:
http://postscapes.com/internet-of-things-market-size
Peter Middleton, Gartner:
“By 2020, component
costs will have come
down to the point that
connectivity will become a
standard feature, even for
processors costing less
than
$1
“
IoT and Big Data
Networked
heating systems
Networked
surveillance systems
Connected
vehicles
Smart sensor
platforms
Network
capability of
devices
Low power
consumption
Small form
factor
Energy
harvesting
capability
Wireless
technologies
Applications
Appropriate
cost
Enablers
IP is a key driver of innovation
IoT and Big Data
Bosch Group – Example Products
IoT and Big Data
THING IT
[HW | SW]
THING-BASED
FUNCTION
[Local | Business
models known]
IT-BASED
SERVICE
[Global | Business
models required]
IoT Formula for Success
Example SERVICE: Send ambulance
in case of accident (detected by sensors)
Example FUNCTION:
Drive from A to B
A B
Source: University of St. Gallen, Prof. Dr. Elgar Fleisch
IoT and Big Data
Key Drivers in the IoT Ecosystem
Enterprises
 Process efficiency
 End-to-end processes
 STP
 Real-time decisions
 Big Data
Partners
 Value chain optimization
 Real-time integration
 Flexible supply chains
 Mobile tracking &
monitoring
Users
 Mobile revolution
 Social & business networks
 Personalized services
 Location based services
Things
 Ubiquitous comms: GSM, WiFi,
BlueTooth, ZigBee, NFC, RFID
 HW: Cost , performance 
 Software: Embedded Linux,
embedded Java, …
 Smart devices, sensors,
actuators
IoT and Big Data
Vehicle
 Equipped with telematics unit
 Sensors to monitor moving parts,
hydraulics liquids, etc
Partners
 Service provider
 Repair specialist and vehicle
manufacturers
Vehicle Driver
 On-board diagnostics
 Information about other vehicles,
e.g. to unload harvest
Vehicle Operations
 Intelligent monitoring of machine
KPIs and fluid analysis
 Optimum servicing intervals
Example: Remote Condition Monitoring
IoT and Big Data
So this brings us to…
IoT and Big DataTensHundredsThousandsMillionsBillionsConnections
Internet of Things
Machine-to-Machine
Isolated
(autonomous, disconnected)
Monitored
Smart Systems
(Intelligence in Subnets of Things )
Telemetry
and
Telematics
Smart Homes
Connected Cars
Intelligent Buildings
Intelligent Transport
Systems
Smart Meters and Grids
Smart Retailing
Smart Enterprise
Management
Remotely controlled
and managed
Building
automation
Manufacturing
Security
Utilities
Internet of Things
Sensors
Devices
Systems
Things
Processes
People
Industries
Products
Services
Growth in connections generates
an unparalleled scale of data
Source: Machina Research 2014
IoT and Big Data
A new mindset and technology is
required for IoT
A changing approach to
databases in the Internet of
Things
IoT and Big Data
New requirements in enabling technologies
Devices
Connectivity
Platforms
Internet of
Things
Connected things,
products, services,
systems, etc.
Security
Networks
Apps &
Analytics
Databases
Source:
Machina Research 2014
IoT and Big Data
Data
Big data
Changing data
models
Real-time
Processing
Aggregation
Internet of Things
Large estates of
devices
Evolving applications
All forms of data
Data streaming and
processing
Pre-IoT (M2M)
Limited estate of
devices
Single purpose
applications
Structured / Semi-
structured
Data transfers
(sensors and
actuators)
Evolution from M2M to IoT and Big Data
Source: Machina Research 2014
IoT and Big Data
Data
Big data
Changing data
models
Real-time
processing
Aggregation
Databases will need to address new requirements
Scalability
Flexibility
Analytics
Unified View
Source: Machina Research 2014
IoT and Big Data
Scalability
Heterogeneity
Agility & Flexibility
in
Applications, Devices
and Connectivity
Scalability
Flexibility
Analytics
Unified View
in
Data
M2M & IoT Application
Platforms
Data Databases
SQL
(Oracle, IBM, etc.)
for structured data
Hybrid
(SAP Hana, VoltDB, etc.)
for speed and heterogeneity
NoSQL
(MongoDB, Cassandra, etc.)
for agility and heterogeneity
IoT-Platforms and Database-Systems
Source: Machina Research 2014
IoT and Big Data
IoT Foundation: Bosch Suite for IoT
A
D
C
B
Scale
Flexibility
Analytics
Unified View
IoT and Big Data
Example Applications
IoT and Big Data
Event Correlation
IoT and Big Data
Use Case 1: Retail & Logistics
IoT and Big Data
Use Case 2: Handheld Power Tools
IoT and Big Data
Use Case 3: Field Data Capturing
Project SCFD
 Structured Capturing of
Field Data
 Components: Car brakes,
power steering, etc.
 Usage patterns:
temperature, voltage, etc.
 Predictive maintenance,
product optimization
Why MongoDB:
Constantly evolving system,
from a data capturing and a
data analytics point of view
 Large amount of streaming
data
Asset
Management
Stream
Processing
Big Data
Management
Analytics
BRM BRM
IoT and Big Data
Capabilities
Solutions
Key Capabilities of MongoDB
Bosch SI IoT Suite
M2M | BPM | BRM | Big Data
A
D
C
B
Scale
Flexibility
Analytics
Unified View
IoT and Big Data
Register Now for Webinar 2
Key Data Management Capabilities
for IoT
IoT and Big Data
Any questions?

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Internet of Things and Big Data: Vision and Concrete Use Cases

  • 1. IoT and Big Data Internet of Things and Big Data Part I / III: Vision and Concrete Use Cases
  • 2. IoT and Big Data Joint Webinar Series: Register to Reserve your Seat Webinar 1: Vision & Use Cases Webinar 2: Data Management Requirements for the IoT Webinar 3: From Concept to Code
  • 3. IoT and Big Data Today’s Speakers Dirk Slama, Director of Business Development Bosch Software Innovations 20+ years of experience in large-scale distributed application projects including M2M and IoT projects Emil Berthelsen, Principal Analyst Machina Research Two decades of experience with management, strategic and research consulting in business process management, telecoms and IT and more recently focused on M2M, IoT and Big Data Mat Keep, Product Management & Marketing MongoDB 15 years working in high scale, data driven systems, working with relational databases before moving into NoSQL and Big Data platforms
  • 4. IoT and Big Data "Fixed" computing Mobility/BYOD Internet of things Internet of everything Source: Cisco IBSG, 2013 (you go to the device) (the device goes with you) (age of devices) (people, process, data, things) 1995 2000 2013 2020 200M 10B 50B Rapid growth of connected things
  • 5. IoT and Big Data IoT Predictions (by 2020-22) 7,1tn IoT Solutions Revenue | IDC Some Big Numbers: 1,9tn IoT Economic Value Add | Gartner 309bn IoT Supplier Revenue | Gartner 50bn Connected Devices | Cisco 14bn Connected Devices | Bosch SI Some small numbers: http://postscapes.com/internet-of-things-market-size Peter Middleton, Gartner: “By 2020, component costs will have come down to the point that connectivity will become a standard feature, even for processors costing less than $1 “
  • 6. IoT and Big Data Networked heating systems Networked surveillance systems Connected vehicles Smart sensor platforms Network capability of devices Low power consumption Small form factor Energy harvesting capability Wireless technologies Applications Appropriate cost Enablers IP is a key driver of innovation
  • 7. IoT and Big Data Bosch Group – Example Products
  • 8. IoT and Big Data THING IT [HW | SW] THING-BASED FUNCTION [Local | Business models known] IT-BASED SERVICE [Global | Business models required] IoT Formula for Success Example SERVICE: Send ambulance in case of accident (detected by sensors) Example FUNCTION: Drive from A to B A B Source: University of St. Gallen, Prof. Dr. Elgar Fleisch
  • 9. IoT and Big Data Key Drivers in the IoT Ecosystem Enterprises  Process efficiency  End-to-end processes  STP  Real-time decisions  Big Data Partners  Value chain optimization  Real-time integration  Flexible supply chains  Mobile tracking & monitoring Users  Mobile revolution  Social & business networks  Personalized services  Location based services Things  Ubiquitous comms: GSM, WiFi, BlueTooth, ZigBee, NFC, RFID  HW: Cost , performance   Software: Embedded Linux, embedded Java, …  Smart devices, sensors, actuators
  • 10. IoT and Big Data Vehicle  Equipped with telematics unit  Sensors to monitor moving parts, hydraulics liquids, etc Partners  Service provider  Repair specialist and vehicle manufacturers Vehicle Driver  On-board diagnostics  Information about other vehicles, e.g. to unload harvest Vehicle Operations  Intelligent monitoring of machine KPIs and fluid analysis  Optimum servicing intervals Example: Remote Condition Monitoring
  • 11. IoT and Big Data So this brings us to…
  • 12. IoT and Big DataTensHundredsThousandsMillionsBillionsConnections Internet of Things Machine-to-Machine Isolated (autonomous, disconnected) Monitored Smart Systems (Intelligence in Subnets of Things ) Telemetry and Telematics Smart Homes Connected Cars Intelligent Buildings Intelligent Transport Systems Smart Meters and Grids Smart Retailing Smart Enterprise Management Remotely controlled and managed Building automation Manufacturing Security Utilities Internet of Things Sensors Devices Systems Things Processes People Industries Products Services Growth in connections generates an unparalleled scale of data Source: Machina Research 2014
  • 13. IoT and Big Data A new mindset and technology is required for IoT A changing approach to databases in the Internet of Things
  • 14. IoT and Big Data New requirements in enabling technologies Devices Connectivity Platforms Internet of Things Connected things, products, services, systems, etc. Security Networks Apps & Analytics Databases Source: Machina Research 2014
  • 15. IoT and Big Data Data Big data Changing data models Real-time Processing Aggregation Internet of Things Large estates of devices Evolving applications All forms of data Data streaming and processing Pre-IoT (M2M) Limited estate of devices Single purpose applications Structured / Semi- structured Data transfers (sensors and actuators) Evolution from M2M to IoT and Big Data Source: Machina Research 2014
  • 16. IoT and Big Data Data Big data Changing data models Real-time processing Aggregation Databases will need to address new requirements Scalability Flexibility Analytics Unified View Source: Machina Research 2014
  • 17. IoT and Big Data Scalability Heterogeneity Agility & Flexibility in Applications, Devices and Connectivity Scalability Flexibility Analytics Unified View in Data M2M & IoT Application Platforms Data Databases SQL (Oracle, IBM, etc.) for structured data Hybrid (SAP Hana, VoltDB, etc.) for speed and heterogeneity NoSQL (MongoDB, Cassandra, etc.) for agility and heterogeneity IoT-Platforms and Database-Systems Source: Machina Research 2014
  • 18. IoT and Big Data IoT Foundation: Bosch Suite for IoT A D C B Scale Flexibility Analytics Unified View
  • 19. IoT and Big Data Example Applications
  • 20. IoT and Big Data Event Correlation
  • 21. IoT and Big Data Use Case 1: Retail & Logistics
  • 22. IoT and Big Data Use Case 2: Handheld Power Tools
  • 23. IoT and Big Data Use Case 3: Field Data Capturing Project SCFD  Structured Capturing of Field Data  Components: Car brakes, power steering, etc.  Usage patterns: temperature, voltage, etc.  Predictive maintenance, product optimization Why MongoDB: Constantly evolving system, from a data capturing and a data analytics point of view  Large amount of streaming data Asset Management Stream Processing Big Data Management Analytics BRM BRM
  • 24. IoT and Big Data Capabilities Solutions Key Capabilities of MongoDB Bosch SI IoT Suite M2M | BPM | BRM | Big Data A D C B Scale Flexibility Analytics Unified View
  • 25. IoT and Big Data Register Now for Webinar 2 Key Data Management Capabilities for IoT
  • 26. IoT and Big Data Any questions?

Editor's Notes

  • #5: Kannst du die Abbildung neu erstellen und Cisco als Quelle nennen?
  • #8: Drucksensor BMP085 von Bosch Sensortec setzt Maßstäbe bei Packungsdichte, Ruhestrom und Auflösung Der neue digitale MEMS Drucksensor BMP085 von Bosch Sensortec (5 x 5 x 1,2 Millimeter) unterstützt unter anderem den Trend, Navigationsfunktionen in Mobiltelefone zu integrieren. Mit seiner sehr feinen Höhenauflösung von bis zu 25 Zentimetern ermöglicht er eine sinnvolle Unterstützung bei einem fehlenden GPS Signal. Über die ROHS-Konformität hinaus ist der Sensor auch frei von Halogenen. UNIMAT Heating Boiler UT-M The UT-M boiler series, as flame-tube smoke-tube boilers, built in accordance with the Pressure Vessels Directive, is used to produce high-pressure hot water cheaply in the mid-temperature range up to 190°C. http://www.bosch.com/en/com/innovation/insidebosch/powertrains_of_tomorrow/challenges_posed_by_the_electric_powertrain/challenges_posed_by_the_electric_powertrain.html Challenges posed by the electric powertrain Powerful batteries, the latest power electronics: the electric drive presents engineers with many new challenges.
  • #9: Within the Internet of Things, many if not all objects of our daily life will become „smart“. What does this mean? According to Prof. Elgar Fleisch from University of St. Gallen, „Smart Things“ are defined by the combination of „Things“ with IT Hardware and Software. This allows for two kinds of functions: first the original „primary“, „thing-based“ functions. Those are usually local functions, with known business models. A car drives you from A to B, a phone lets you make phone calls, a watch gives you the time, your glasses allow you to see. New is the second area: by adding IT based services via the IoT, many new additional „secondary“ functions become possible. They are in many cases not limited to the local physical device, and come often in combination with new business models. Examples: a car in an emergency may call the red cross service. Its sensor data may be used to warn other cars behind you about foggy or icy road stretchs. Floating position data from cars and phones may be used to get information about traffic congestions. Your watch will allow for remote monitoring of your health and give you an early warning before a stroke. And so on. Those are just a few example of new functions, that become possible with the Internet of Things – new chances to make lifes better, and certainly also significant new business opportunities for companies to create new customer offerings based on the combination of smart things and web-based services.
  • #25: MongoDB provides agility, scalability, and performance without sacrificing the functionality of relational databases, like full index support and rich queries Indexes: secondary, compound, text search, geospatial, and more