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

NinoGuarnacciSolu-onArchitectIoT&Mobilenino.guarnacci@oracle.com

Agenda

§ Context

§ Architecture

§ Use-Cases

§ Demo

People & Apps

Processes

Step 3Step 3Step 2Step 1

Data & Analysis

PhysicalDevicesandObjectsConnectedtotheInternetandEachOtherforIntelligentDecisionMaking

DeliveringtheRightInforma>ontotheRightPerson(orMachine)attheRightTime

LeveragingDataintoUsefulInforma>onforDecisionMaking

Connec>ngPeopleinMoreRelevant,ValuableWays

WhatistheInternetofThings

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)

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)

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)

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

Agenda

§ Context

§ Architecture

§ Use-Cases

§ Demo

•  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.

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

•  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

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

VIRTUALIZE COMMUNICATE

SECURE

Quicklycollectdatafromdevices

Accelerateapplica>ondevelopment

Deliverhigh-speedmessaging

Scaletomillionsofend-points

Buildtrustedexchange

Protectend-to-enddataflow

ANALYZE

ANALYZEATREALTIME ENRICH

Turndataintoac>onableinsight

Enableedgeintelligence

AddGeodataforfastROI

Enablecontextualizedsearch

EnableBIandBigDataAnalysis

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

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

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

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

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