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

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

    1. 1. IoT and Big Data Internet of Things and Big Data Part I / III: Vision and Concrete Use Cases
    2. 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. 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. 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. 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. 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. 7. IoT and Big Data Bosch Group – Example Products
    8. 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. 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. 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. 11. IoT and Big Data So this brings us to…
    12. 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. 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. 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. 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. 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. 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. 18. IoT and Big Data IoT Foundation: Bosch Suite for IoT A D C B Scale Flexibility Analytics Unified View
    19. 19. IoT and Big Data Example Applications
    20. 20. IoT and Big Data Event Correlation
    21. 21. IoT and Big Data Use Case 1: Retail & Logistics
    22. 22. IoT and Big Data Use Case 2: Handheld Power Tools
    23. 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. 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. 25. IoT and Big Data Register Now for Webinar 2 Key Data Management Capabilities for IoT
    26. 26. IoT and Big Data Any questions?

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