Warp 10: the key enabler of digital transformation of IoT business
From sensors to industrial processes, from monitoring to cybersecurity,
Warp10 proposes a disruptive software technology which makes you to consider
data and artificial intelligence in a new and different perspective for IoT business
Cityzen Datawww.cityzendata.com / www.warp10.io
Warp10
Warp 10: A powerful and disruptive software
platform to address sensors / IoT / Machine Data
#1
3Machine Data
Analytics
Four generic families of Data
#1 Data from major companies databases
Big Data = Data MiningNo Disruption
#2 Data from Social Networks, mails …
Semantic, Content analytics
RelationalSQL
NoSQL
#3 Data for Sensors, Meters, Mobiles …
Where IoT Big Data is
NoSQLTime Series
#4 Geospatial databases
Structured Géospatial3D Modeling
Iot Data=MachineData=TimeSeries
examples
When traditionalITapplicationisbasedonrelational databases,IoT requires othertechandmoreflexible approaches
Warp10
4Machine Data
Analytics
Warp10/Asset #1:Geo TimeSeries™
+
Adisruptivearchitectureforsensordata
5Machine Data
Analytics
Warp10/Asset #2:Analytics &Language
800 functions
AefficientframeworkforIoT applicationstodevelopfasterandtofocusontheircorebusinessfirst
From summary statistics to advanced signal processing and pattern detection
A stack based language
dedicated to time
series analytics
Things/Sensors
Datatransmission
Datacleansing
Datasynchronisation
Statistics,patterndetection,machine
learning, correlations,anomaliesdetection…
Datafiltering
BusinessApplication
Predictive
Warp10
6Machine Data
Analytics
Warp10/Asset #3:HighLevel Security
Secured by Design
Metadata encrypted by default Total data encryption in optionDynamic allocation of cryptographic tokensData access by token holders tracked Data portfolio management
TotalcontrolguaranteedbyWarpScript language>Keydifferentiator
7Machine Data
Analytics
Machinelearningtasks:- Sensors&Multisensors machine
behaviorunderstanding- Classification,regression,clustering- Anomaly,failure,faultdetection- Datavisualization
Tree-basedAlgorithms- Randomforests- Gradientboostedtrees
Deeplearning- Artificialneuralnetworks
(perceptron,convolutional,recurrent,longshorttermmemory,...)
- Generativeadversarialnetworks
StatisticalLearning- Supportvectormachine- BayesianNetworks- Stochasticcontrol/Markov
decisionprocessesDimensionalityreduction- t-distributedstochasticneighbor
embedding(t-SNE)- Principalcomponentanalysis- Independentcomponentanalysis- Laplacianeigenmaps- Isomap
Warpscriptproposesgenericextensionstolearn,toreproduce,todetect,topredict,ortosimulate
patterns,behaviors,outliers,anomalies,failures…
FromBigDatatoArtificialIntelligence…orHowtotakeprofitofcomplex algorithms inbusiness applications
8Machine Data
Analytics
3offers
Standalone Version
DistributedVersion
Embedded Version
HA
Datalog
9Machine Data
Analytics
OpenSourceDistribution
http://www.warp10.io
10Machine Data
Analytics
Cloud or On Premise IT
Telecoms
To an horizontal business value added for apps
Sensors Meters IoT
- - - - - - DATA - - - - - -
Energy App
Transport App
HealthApp
Warp10 by Cityzen DataA horizontal, neutral and
industrial approach on the sensors and IoT market
Cityzen Data ingests and
manages more than 250 Bn
measures / day in SaaS mode
(Sept 2016)
Cityzen Data works with major companies
When many companies claim they have IoTData platform, we can prove that we have the best and the most promising one.
GE, Airbus, Orange, Amazon, OVH , Cap Gemini …
Cityzen Data helps clients to get value from their dataCityzen Data is a tech companies
without any interference with client own business.
Businesses functions, algorithms and services developed by clients remain
their property.
100,000 to 1.5 million measures / sec / Core
AkeyvalueboosterforgettingvaluefromIoT &eventsdata
11Machine Data
Analytics
Ranked #5
Warp10designed byMathiasHerberts,Co-founder, CTO
Warp10
Warp10 Use
Cases#2
13Machine Data
Analytics
UseCase#1:Energyonthewholevaluechain
TransmissionMicrophasers
IngestingRenewable energiesfromsmallsources tobiggerones inrealtime
Market/trading
IndustrialConsumption
Cities,Neighborhoods,Buildings
Storage
HomeConsumption
14Machine Data
Analytics
UseCase#2:SmartCity/Mobility
Futuremobility istypicallythe usecasethatrequirestocrossalargerangeofdata.WithCIRBinBrussels, Cityzen Dataaimsto:• Tobuilduphorizontal andscalabledatainfrastructure• Toanalyzedatainorderto improvetrafficmanagement andtopropose servicestocitizens
Multimodal mobility management basedongathering andcrossing datafrom:- Public transport timetables- Public transport realtimedata- Trafficmonitoring- Trafficlightscontrol- Trackers- Weather forecasts- Airquality- Electricvehiclesstationsmonitoring- Bikesharing stationmonitoring- Carparks- Smartphones- Roads&streetsworksstatus…
15Machine Data
Analytics
UseCase#3:SmartBuilding/SmartFactories
SmartBuilding andSmartFactoriesarecontrolled byalargerangeof sensors indifferent areas:electricity, lighting,heating, water,cooling,security….
Storing andcrossingdataallowtogetvaluefromalargeamount ofexistingdata
16Machine Data
Analytics
Source:Airbus
UseCase#4:Aeronautic maintenance
17Machine Data
Analytics
ASelf-DrivingCarWillCreate 1Gigabyte ofDataPerSecondhttps://datafloq.com/read/self-driving-cars-create-2-petabytes-data-annually/172
Lastyear,anestimated 26millionconnected carscollected morethan 480Terabytes ofdata.Thatnumber is expected toincrease to11.1petabytes by2020.Someplug-inhybrid vehicles arecapableofgenerating upto25Gigabytesofdatainjust onehour.
http://www.ibmbigdatahub.com/blog/how-vehicle-telematics-changing-auto-industry
http://www.economist.com/blogs/economist-explains/2013/04/economist-explains-how-self-driving-car-works-driverless
UseCase#5:ConnectedCars
18Machine Data
Analytics
UseCase#6:Groupama SailingTeam
Dataanalytics:- Toimproveperformances
- Topredictproblems
America’sCupBermuda2017
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Analytics
UseCase#7:Systems,IT,TelecomMonitoring
Warp10crossandanalyzesdatacomingfromanytypeofsensors, subsystems,scada …todetectfault,anomalies,discordsortopredictfuture troubles
Monitoringofsystemsisdonebysensorsmeasuring
alargerangeofparameters:mechanic,
power,datatransmission,dataprocessing, latency…
20Machine Data
Analytics
UseCase#8:Majorgroupinvestingworldwideinagriculturecompanies(2017)
DataAnalyticstopreventrisksontheyieldsandmarketvalueofproduction
Warp10leveragedtocrossandanalyze:- Datacomingfromlocalsensors- Datageneratedbyequipment(manurespreaders,irrigation…)
- Externaldatalikeweatherforecast
21Machine Data
Analytics
UseCase#9:Health,wellnessandsport
TheoriginalusecaseforCityzen Data:gettingdatafromsmarttextileinorder:
- Tounderstandbodybehavior- Todetecthistoricalandpersonalpatterns- Toanticipatespecifictroublesordiseases
- …
MovementsHeartrate/ECGTemperaturePH
22Machine Data
Analytics
UseCase#10:Security/Cybersecurity
Securityandcybersecurity needtocrossdatafromalargerangeofsources.
GeoTimeSeries™ technologies isparticularly relevant todetectweaksignals amongtheoceanofvarioustypes ofdata.
TheWarp10technology isusedtomonitor Internetaccesses.
Internet accessInternet surfingSmartphonesSocialmediaPersonalconnectionsPhone callsMailsPaymentsHarddisks contentsBorderscontrolsAirport controlsInterviewminutesVideosecurity
23Machine Data
Analytics
UseCase#11:Conditional&PredictiveMaintenance
Togetrealvalue inmaintenance, sensorsdataneed:
Tobesynchronized
Tobehistoricized
Tobecleaned
Tobemodeled
Tobecrossedwith other data
Tobeinterpreted inrealtime(andinbatchmode)
Cityzen Dataprovides allthese featuresthrough “on theshelves” tools &algorithmstodifferentsectors: industry, power generators,cars,aeronautics, telecoms…
Astrongtrend:“WherePredictiveAnalyticsIsHavingtheBiggestImpact”.HarvardBusinessReview
24Machine Data
Analytics
UseCase#12:Testbeds
Testbeds arefacingtothe increasingofthenumber ofphysicalsensors andsystemsprobes. Theyproduces ahugeamount ofmeasures.
Warp10 provides “onthe shelves”featuresandtools totestbeds experts togetbenefit from:- Dealingwith alargeamount ofdata- Cleaningandsynchronizing highfrequency data- Detectingweaksignals- Takingprofitofdatahistorian
Beyond the tech, a new paradigm in IT
architectures#3
26Machine Data
Analytics
From transactionstoevents streams
Business Process
Assets
HRDevices
Sensors
Systems
IT architecture driven by transactions
Rigid business applications & databases
IoT
Sensors
Events
Transactions
IT architecture driven by streamof events
Flexible and scalable architecture
Operations
CityzenDa
ta
27Machine Data
Analytics
Legacydatatechnologiesarenotadaptedtonewdatachallenges
Dataare« owned »byabusinessorspecificentity
1- OwnershipDatarefertoaspecificformatandontology
2- Interoperability
Thirdapplicationsdecreaseprimaryapplications
performances
3- Loadbalancing « Nonrelevant »dataareremovedbyprimary
applications
4- MissingInformation
SQLtechnologieshaveweakperformanceforIoT Data
5- Performances
Difficulttofacetonewandunknownfuturesourcesof
data
8- Nonflexibility
Datamanagementtiedtovendorsapplications
7- NonindependencySQLBIrequestheavyextractionoperation(Datawarehouse)
6- Rigidextractions
28Machine Data
Analytics
Warp10technologyaddressesallthesechallenges
Dataclosetorowdataareeasiertobemanagedregardingownership
1- Ownership
Timeseriesdataaredefinedbya universalformat
2- Interoperability
« Timeseries »architecturesarescalable
3- Loadcomputing Alldata– evenmistakes–arestored
4- Missinginformation
Hig levelperformancesforIoT Data(x10,x100)
5- Performances
Naturalingestionofnewsourcesofdatawithoutany
impact
8- Flexibility
Neutralarchitectureregardingapplications
7- Independency
Dynamicaccessestoselecteddata
6- Ontheflyaccesses
29Machine Data
Analytics
TimeSeries:theonlyrealisticandefficientwaytobreakthesilos
Data is mainly considered as a property belonging to a specific
unit within an organization
TODAY
Data result from one application when it is required to address
new usages and services
Development of new applications requires complex accesses to existing data or interfaces with
existing data or applications
Time Series can be a way to store raw data and favors an global
asset approach
TOMORROW
Historian of raw data is the key to develop applications and services
when needs occur
New applications and services can be developed in a short time
06/12/2016Cityzen Data30
IamcomingoutfromaWarp10@cityzendatapres…Iamfeeling likeHowardCarteropeningTutankhamuntomb…itwillnolongerbethesame
