IoT Realized - The Connected Car

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  • SPRINGONE2GX WASHINGTON, DC

    Unless otherwise indicated, these sl ides are 2013-2015 Pivotal Software, Inc. and l icensed under a Creat ive Commons Attr ibut ion-NonCommercial l icense: ht tp: / /creat ivecommons.org/ l icenses/by-nc/3.0/

    IoT Realized The Connected Car

    By Phil Berman, Michael Minella, and Derrick Shields @pivotalphil, @michaelminella

  • Michael MinellaTwitter: @michaelminellaPodcast: http://javaOffHeap.com or @OffHeapWebsite: http://spring.ioDerrick ShieldsWebsite: http://pivotal.io

    Phil BermanTwitter: @pbermanWebsite: http://pivotal.io

  • https://github.com/pivotal/IoT-ConnectedCar

  • B A C K T O T H E BEGINNING

  • REALLY COOL IoTPROJECT

  • COVERED BY AN

    NDA

  • CONNECTED

    CAR

  • P R E D I C T T H E DESTINATION

  • PREDICT THE RANGE

  • PIECES ARE THE

    SAME

  • I o T I S A B O U T OPERATIONAL EFFECIENCIES

  • GE Video?

  • 1%I S A BIG DEAL

  • W O R L D W I D E 1 BILLION CARS

  • 2 0 3 5 2 BILLION CARS

  • BARRELS PER DAY120 MILLION

  • AS MUCH CARBON AS

  • CONNECTED

    CAR

  • Storage

    Ingest Process Analyze Edge

    Applications React

  • Edge

    ApplicationsReact

    Process Analyze

    Storage

    Ingest

  • HOW DOES THIS

    WORK?

  • START IN THE

    CAR

  • ON BOARD DIAGNOSTICS

    OBD II

  • 01 0D !01 0D !18 DA F1 11 03 41 0D 30 !> !

  • PHONE PROVIDES CONNECTIVITY

    127.0.0.1:9000

  • { ! "vehicle_speed":103, ! "obd_standards":2, ! "intake_manifold_pressure":"", ! "accelerator_throttle_pos_e":14, ! "engine_load":89, ! "maf_airflow":33, ! "latitude":"32.897554", ! "vin":"1HGCM82633A004352", ! "bearing":"343.922580", ! "catalyst_temp":779, ! "relative_throttle_pos":12, ! "fuel_level_input":89, ! "fuel_system_status":[2,0], ! "accelerator_throttle_pos_d":29, ! "acceleration":"0.953", ! "throttle_position":21, ! "barometric_pressure":97, ! "control_module_voltage":13, ! "longitude":"-96.810236", ! "distance_with_mil_on":0, ! "coolant_temp":94, ! "intake_air_temp":34, ! "rpm":1593, ! "short_term_fuel":-2, ! "time_since_engine_start":4054, ! "absolute_throttle_pos_b":38, ! "long_term_fuel":3 !} !

  • TCP SOCKET OVER

    BLUETOOTH HTTP POST

    OVER CELLULAR

  • O N T H E SERVER

  • EASY1

  • MATURE2

  • NOT DEPENDENT3

  • SPRING CLOUD STREAM

  • EVOLUTION OF DATA APPLICATIONS

  • MONOLITHS

  • EXISTINGINTEGRATION AND BATCH

  • DATAMICROSERVICES

  • CLOUDNATIVE

  • DEVELOPED AND TESTEDIN ISOLATION

  • APPLY MICROSERVICESPATTERNS

  • OPERATIONALLY EASY TO

    GOVERN

  • DATA INTEGRATIONAS A SERVICE

  • COMPOSTION OFMICROSERVICES

  • ZERO CODING

  • OPERATIONAL ANDORCHESTRATION COVERAGE

  • WARS ANDAPP SERVERS

  • DECOMPOSE INTO BOOTMICROSERVICES

  • SPRING CLOUD STREAMS

  • SPRING XDEXTREME DATA

  • Processor Sink

    Bus

    Source

  • transformer

    Redis

    http

    filter

    hdfs type-transformer

    python gemfire

  • transformer http filter hdfs

    type-transformer

    python

    Gemfire

    REST

    Hadoop

    Gemfire

  • transformer http filter hdfs

    type-transformer

    python

    Gemfire

    REST

    Hadoop

    Gemfire

  • /** ! * Perfo

    rms the domain t

    ransformation in

    to an acme motor

    s specific domai

    n

    model. ! * ! * @auth

    or gfoster!

    * ! */ !public c

    lass AcmeMotorEn

    richingTransform

    er implements Tr

    ansformer{ !

    ! @Bean !public M

    essage

  • REALTIME DATASCIENCE

  • 1PREDICTJOURNEY

  • 2PREDICTRANGE

  • HOW DOES I T

    WORK?

  • STORE SENSOR

    DATA

  • OFFLINE BATCH TRAINING

  • J O U R N E Y CLUSTERS

  • INITIAL PREDICTION

  • DRIVING HOME TO WORK

  • DRIVING WORK TO HOME

  • !"Predictions": { ! "ClusterPredictions": { ! "0": { ! "EndLocation": [ ! 32.98525175453122, ! -96.70940837440399 ! ], ! "MPG_Journey": 25.60900810315203, ! "Probability": 0.63736 ! }, ! !

  • REALTIME EVALUATION

  • 0.05

    0.10

    0.15

    0.20

    0.25

    0 1 2 3 4 5 6 7 8 9 10Minute

    Med

    ian

    Log

    Loss

    PERFORMANCE OF CLASSIFICATION

  • RANGE PREDICTION

  • R E A LT I M E DASHBOARD

  • GEMFIRE1

  • REST2

  • YOEMAN3

  • ANGULARJS4

  • SOLUTION AT SCALE

  • 100,000s OFCLIENTS

  • MILLIONS OFMESSAGES

  • PER MINUTE OVER

    100 GB

  • IoT

    ISSUES

  • COMPATIBILITY

  • CONNECTIVITY

  • SECURITY

  • A D D I T I O N A L USE CASES

  • Q U A L I T Y FEEDBACK

  • F L E E T MANAGEMENT

  • Spring XD

    Data Science Data Warehouse

    Gemfire

    Greenplum

  • A C C I D E N T ASSISTANCE

  • IoT PRESENTS UNIQUE CHALLENGES

  • IF YOU CANBRIDGE THE GAPS

  • SPRING CLOUD MAKES THE REST EASY

  • Unless otherwise indicated, these sl ides are 2013-2015 Pivotal Software, Inc. and l icensed under a Creat ive Commons Attr ibut ion-NonCommercial l icense: ht tp: / /creat ivecommons.org/ l icenses/by-nc/3.0/ 98

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