2° sessione oracle - crui: internet of things in a real-world, use cases and components

NinoGuarnacciSolu-onArchitectIoT&[email protected]

Copyright©2015Oracleand/oritsaffiliates.Allrightsreserved. OracleConfiden>al–Internal

Agenda

§ Context

§ Architecture

§ Use-Cases

§ Demo

2


People & Apps

Processes

Step 3Step 3Step 2Step 1

Data & Analysis

PhysicalDevicesandObjectsConnectedtotheInternetandEachOtherforIntelligentDecisionMaking

DeliveringtheRightInforma>ontotheRightPerson(orMachine)attheRightTime

LeveragingDataintoUsefulInforma>onforDecisionMaking

Connec>ngPeopleinMoreRelevant,ValuableWays

WhatistheInternetofThings

Copyright©2015Oracleand/oritsaffiliates.Allrightsreserved. OracleConfiden>al–InternalConfiden>al–OracleInternal/Restricted/HighlyRestricted

4

IoTisenablingnewbusinessmodels..

Break/FixorScheduled

Predic-vePrescrip-ve Ownership AsaService

Sta-cAnaly-cs

Real-TimeBig-DataAnaly-cs

CentralService

Self-Serve/Self-GuidedService

Maintenance

BusinessAnaly>cs Service

Consump>onModel


IoTdrivesIndustrialInnova>on(I)

5

Healthcare

•  Remotehealthcareassetmanagementandenableself-service

•  Monitorpa>entbehaviorinreal->me

ConsumerProducts

•  Proac>veremotemaintenanceandservice

•  Replacementsupplytracker

•  Consumerhabitstracking

Manufacturing

•  Sensorstrackproductsacrosssupplychains

•  ImproveInventoryManagement

•  Reducelogis>calcostsandcapitalexpenditure

Oil&Gas/U-li-es

•  Automa>on,controlandmonitoringofpipelines,oiltanks

•  Assettracking


IoTdrivesIndustrialInnova>on(II)

6

Publicsector

•  Wastemanagement

•  Trafficmanagement

•  Cityautoma>on(parking,...)

Banking&Insurance

•  Usage-basedinsurance•  NewPOS•  Frauddetec>onandassetrecovery

Retail

•  Shoppinganaly>cs•  Vendingmachineremotecontrolling

•  ImproveInventoryManagement

•  DigitalSignage

Realestate/Domo-cs•  Smartligh>ng•  Assetcontrol,monitoringandautoma>on

•  Access&physicalsecurityimprovement

•  HVACautoma>on


IoTdrivesIndustrialInnova>on(III)

7

Logis-cs

•  Assettracking•  Flowsop>miza>on

•  Supplychainautoma>on

Agriculture

•  Livestocktracking•  Irriga>on•  Supplychainautoma>on

Manyotherindustries/subindustriesimpactedincoreandnon-coreprocesses


SanFranciscoParking

•  Theglobalfirstimplementa>onofacitywide“smart”parkingmanagementsystemandtechnologytomanageparkingsupplyanddemandmoreintelligently.

•  “Wehavesensorsat8,200ofthecity’s27,000meteredparkingspaces,andwegetinforma>onfromthegatearmsatthecity’s14garages,whichamongthemhaveabout13,000spaces

SmartCity:ParkingManagement

8


Agenda

§ Context

§ Architecture

§ Use-Cases

§ Demo

9


•  BegerCustomerExperience

•  NewBusinessModels

•  Opera>onalImprovements

•  IncreasedEfficiency

GrowRevenue

ReduceCost

2mainBusinessBenefitspillarsdrivingIoTadop>on

•  HVAC•  Ligh>ng•  SafetyandSecurity•  ResourceU>liza>on•  Maintenance

•  Preventa>veMaintenance•  RemoteDiagnos>cs•  SafetySystems•  V2XandC2X•  FleetandUseManagement

ShareholderValue


RewardInfo&DecisionMaking

Facilita>on Service&Innova>on

RewardInformation &

Decision Making Facilitation Innovation


REWARDCONSUMERSFORTHEIRTIME,MONEY,EFFORT,ANDENGAGEMENT

PactAppprovidesaservicetobusinessesinwhichemployeesenrolledinthecompany’shealthplancommittoacertainnumberofac>vedaysperweek,andaresubsequentlyrewarded(orpunished)foreachdaytheymeettheirgoals(ordon’t).Throughloca>on-basedcheck-intracker–-basedlogging,aimstolower(orraise!)deduc>bleswitheachworkoutby$5.

Reward

Promo>onalrewardsintheformofcoupons,discounts,ordeals

Promotion

Gamifica>onrewardscustomersbasedoncontextualelements

Gamification

Brandscanusesensorstorewardconsumersbyentertainingthem

Entertainment


EMPOWERCONSUMERSWITHTHEABILITYTOACCESSANDACTONINTELLIGENCE

Whirlpoolunveiledconnectedwashersanddryersthatmonitorproductperformancedatatodriveecliency.AppliancesareintegratedwiththeNestthermostatwhich,ifsettoAway,ac>vatesanEcoboosttolongerdrying>mesatalowertemperaturetoconserveenergy.Ownerscanaccesstheappliance’sremotelyviatheirmobiledevice.

Information & Decision Making

Contentthataidsinproductandserviceevalua>on.

EvaluationLoca>on-basedaid,typicallydeliveredasnaviga>onorway-finding

Navigation

Monitoringcapabili>esaddedtothingspeoplecareabout

Monitoring

Newsorinforma>onprovidedbysensorsforpublicawareness.

News


FOSTEREASIER,MOREACCESSIBLE,ANDCONVENIENTBRANDEXPERIENCES

Starbucksnowmakestransac>onspossiblebeyondtheStarbucksappthroughthird-partyintegra>ons(extendingbeyondsmartphones)withwearabledevicessuchasMicrosonBand,Samsung’sGalaxyGear,andPebble.Byintegra>ngwithna>vepaymentapps,forinstance,customersareabletopayfortheirbeveragesviathewearable.

Facilitation

IoTexpandsconversioncapabili>esthroughthepurchasefunnel

Conversation

IoThelpsbrandsmaketheirproductsandservicesmoreuseful.

Utility

Iden>tyauthen>ca>onthroughsensordatafacilitatesfastervalida>onoradmissionprocess

Identity

IoTisshininghowbrandsfacilitatecustomerexperienceviathepaymentprocess

Payment


SUPPORTANDRETAINCUSTOMERSBYPROACTIVELYIDENTIFYINGOPPORTUNITIES

ServiceinIoTcanextendtosalesSales

IoTalsoop>mizessupportacrosschannelsanddevices,bothreac>velyandproac>vely

Support IoTenablescustomiza>onandpersonaliza>ontohelpbrandsstayrelevantanddifferen>ated

Customization

Collec>ngfeedbackandinnova>ngproductsandservicescanhappenmorequickly

Feedback

Innovation


Manufacturing–Industry4.0

• Sensorstrackproductsacrosssupplychains

• ImproveInventoryManagement

• Reducelogis>calcostsandcapitalexpenditure


Reactive Maintenance •  Machine fails,

then fix it

Preventive Maintenance •  Based on time or

counter schedules

Condition-based Maintenance •  Based on

monitoring to assess condition

Predictive Maintenance •  Based on models

of the evolution of the conditions of assets

MaintenanceManagingcostsimprovingreliabilityandsafety


World-wideleadingmanufacturerofvalves,automa-oncomponentsandotherproductsforhigh-techprocessindustry

Challenges•  Increaselife>meandreliabilityofcomponentsusedinsafetyandhealth-cri>calchemicalprocesses

Solu-onComponents•  Real->mefilteringandprocessingofeventsfromequipmentdeployedworldwide

•  Integra>onwithCRMandservice>cketsystem

Benefit•  Proac>veand>melypartsreplacementavoidsproduc>ondown>me

•  Increasedknowledgeofproductusageimprovesproductqualityandfunc>onality

18


•  AirbusSASisadivisionofAirbusGroupSEthatmanufacturescivilaircran.ItisbasedinBlagnac,France,asuburbofToulouse,withproduc>onandmanufacturingfacili>esmainlyinFrance,Germany,SpainandtheUnitedKingdom.

•  Strongdemand&compe>>onfromnewplayers(Canada,Brazil&China)

•  Needtodeliveraircranbacklogfaster(~1000newaircrans)

•  Needtoincreaseinnova>veflighttestbutwithless>metoperformit

•  Usecase–  Newsystemneedtobeopera>onal

anddemonstrateperformancetocompletethetestsofA320Neowhosecommercialdeliveryisscheduledtobeginin2015.

–  Increasescalabilityofdatastorage–  Deliverhigherconcurrencyanaly>c

dataaccess–  Scaledataloadingindependently

fromanalysis

•  Benefits–  ImprovedMTBF–  Reduceservicecosts–  Developnewservicerevenue

channels

Airbusop>mizingitsmanufacturingopera>onsviaBigDataontestsensors

Processing/manufacturingProductquality


•  ResponsiveMachinesthatalwaysprovidethemostprofitableproduct

•  Consideringfoodvaria>ons,fluctuatepricingaccordingtodemand

•  Environmentalsitua>onawareness

•  Usecase–  Usage,statusdataprocessedlocally

andintelligently

–  Real>medemandforecastforVending/Fountainbasedonseasonalityandpromo>onaleventsprocessedimmediatelybycorporatedatacenters

–  Responsiveandpredic>vemaintenanceprovidedbasedonindividualmachinerequirements

•  Benefits–  Efficientreplenishmentbasedon

individualuse

–  Immediaterecogni>onofmachinefailure

–  Reduc>onofMTBF

Clientop>mizingusage,forecas>ngandmaintenanceonautomatedVendingMachines

Logis>cs/Distribu>on

Channelautoma-on


Agenda

§ Context

§ Architecture

§ Use-Cases

§ Demo

21


HowdoIcollectdatafromintelligentdevices?Abstractcomplexityassociatedwithdeviceconnec-vityStandardizeintegra-onofdeviceswithenterprise

BuildinganIoTapplica>on:KeyChallenges

HowdoIanalyzeIoTData?Reducenoiseanddetectbusinesseventsatreal--meEnablehistoricalbig-dataanalysisHowdoIintegrateIoTdata&eventswithenterpriseinfrastructure?MakeenterpriseprocessesIoTfriendlyAllowenterprise&mobileapplica-onstocontroldevices


InternetofThingsCloudService

BusinessApplica>onsIoTDevices

Manufacturing,SupplyChain,AssetMgmt

CustomerRela>onshipMgmt,Sales,Service

Ver>calApps–U>li>es,Healthcare,Retail

IoTService

Connect Analyze Integrate/Act


CONNECT

CONNECT

VIRTUALIZE COMMUNICATE

SECURE

Quicklycollectdatafromdevices

Accelerateapplica>ondevelopment

Deliverhigh-speedmessaging

Scaletomillionsofend-points

Buildtrustedexchange

Protectend-to-enddataflow


ANALYZE

ANALYZE

ANALYZEATREALTIME ENRICH

STORE

Turndataintoac>onableinsight

Enableedgeintelligence

AddGeodataforfastROI

Enablecontextualizedsearch

EnableBIandBigDataAnalysis


INTEGRATE

INTEGRATE

INTEGRATEWITHCONNECTORS

INTEGRATEWITHRESTAPIs

CONTROL

QuicklyenrichprocessesandappswithIoTdata

Enablerichanaly>cs

Integratewithanyon-premorSaaSapplica>ons

SupportsOracle&3rdpartyapps

Enableenterprisetocontroldevices

Mobileenabledandpolicydriven


DataDiscovery&Analysis

71◦~183kg

527kg/m40Knots

WindSpeed

SailTension

MastStrain

HullStability

RealTimeFeedback

IoTCloud BigData/Database

EngineeredSystems

Gateway

MastStrain

HydrofoilPosi>on

SailTension

WindSpeed&Direc>on

JavaDevicesSensors

WingPosi>on

HullStability

BoatSpeed

Changestoboat/sailingparametersbasedonAnaly>cs

Challenginginreal->medataprocessing&discovery

Copyright©2015Oracleand/oritsaffiliates.Allrightsreserved. OracleConfiden>al–InternalOracleConfiden>al–Internal/Restricted/HighlyRestricted28

300 IoT sensors all over the boat

3,000 variables about 10 times a second

Video feeds images of the sails every second.

Live metering on the strain, mast, hull, wing

Monitor load generated from the jib to the winches

Constantly adjust the sail wing to wind conditions

Tablets display real-time data and predict what will happen in the next few seconds

“The fact that we can have live data instantaneously is key”

-- Jimmy Spithill, Skipper, ORACLE TEAM USA


Discover&AnalysisonstoredData♫

?

♫

Payment

Geo-Location

•  DiscoverInsights•  AutomatedbatchAnaly>csandReport

•  MachineLearningusingdifferentdatasetsandsources

Passport

Classification

Associate

Report

Analyze

Compare

Discover


StreamAnaly>cs–Whydoesitmager?♫

?

♫

Time WindowsSlide

CorrelateStreamsandSta>cReference(Database,BigData,Cache,Iden>tyDirectoryAggregateandpagernrecognizeheterogeneoussourceamongdifferentconnec>ontypesGeo-Fenceandfilteringeventsusingmanuallydefinedandstandardmaps

MachineLearningonreal->medatausingpre-trainedmodelonhistoricaldata(PMML)

EnterpriseinfrastructurebasedonJ2EE&SparkStreaming

Func>onalresponsivewebbasedinterface

Real-Timeanalysiswithpre-implementedcommonsta>s>calandlogicalfunc>ons

Copyright©2015Oracleand/oritsaffiliates.Allrightsreserved. OracleConfiden>al–Internal31

OracleIoTCloudServiceFeatureSummary

GeoProcessing

StreamingPagernsLibrary

StreamingMachineLearningStreamingBusinessRules

TopologyViewer

Predic-veAnaly-cs

OracleStreamAnaly>cs

DataWarehouse


MachineLearningAnaly>csonStreamingData•  Useofmachinelearningalgorithmstodetectpagerns&performpredic>onsbasedonmodelscreatedfromhistoricaldata

•  Streamingmachinelearningcapableofhandlingveryhigh-throughputstreams,involvetwomainstages:– Modelbuilding(training)

•  Useshistoricaldataasinput–mayinvolvehandlingverylargedatasets•  Performedwithlowfrequency

– Modelscoring•  Performedwithhighfrequency•  Nohistoricaldatarequired-modelrepresenta>oncontainsallrequiredinforma>on

Predic-onstream

MachineLearningPredic-onSystem

Historicaldata–usedtobuildmodel

Streamingdata-predic>onsperformedonit


Anomaly detection using K-Means

•  Anomalies are events that differ or are much less common than the majority

•  K-Means is used in anomaly detection by identifying low density clusters – Clusters with very few members, which equates

to being less common than the majority

•  A common practice is to choose the smallest cluster as an anomaly cluster, assuming it has significantly less members than other clusters


BigData

ERP MES

Grande Roberta

Real-Time

•  Aretherepagernsofeventsthatcausetheequipmenttofail?

•  Whatarethetopfactors/influencersthataffectproductyield?

•  What’sthedownstreamimpactofyieldchangeordefec>veparts?

•  Isthereacorrela>onbetweenmachineparametersandproductquan>ty?

•  Canwepredictthelikelihoodofcertainproductdefects?

•  Arethereassetsusedimproperlyorincau>onareas

AssetAnaly>cs:ComponentsView


Grande Roberta

Real-Time

•  Isthereacorrela>onbetweenmachineparametersandproductquan>ty?

{ "PLANT": "A23", "MACHINE": 34, "PMIN": 110, "ODEV": 0.7, "TEMP": 38, "PRES": 3.4, "STATUS": "PAUSE", "LAT": 14.453, "LON": 42.673 }

120msg/sectelemetryeventsX6machinesent7parameters,oneofthoseiscovarianceparamsindicator

Real-Timeclusteringeventsthroughcovarianceandquan>tydiscoveringlow-densityclustertargetMergeReal-Time&ERPData

AssetAnaly>cs:DemoFlow


Agenda

§ Context

§ Architecture

§ Use-Cases

§ Demo

36

2° sessione oracle - crui: internet of things in a real-world, use cases and components

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