Warp 10: the key enabler of digital transformation in IoT … and IT business
From IoT and sensors to user applications, from maintenance to business services, Warp10 proposes a disruptive software
technology which makes you to consider data analytics and artificial intelligence in a
new and different perspective
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
#1 Data from major companies databases
Big Data = Data MiningNo Disruption
#2Data from documents, social networks, videos …
Content data analytics, semantic
RelationalSQL
NoSQL
#3 Data for Sensors, Meters, Mobiles …
Where future business is
NoSQLTime Series
#4 Geospatial databases
Structured Géospatial3D Modeling
Theradicalchangeinsensor data:TimeSeries
examples
Warp10
Four families of data (including understanding, technologies, culture, governance)
Sensor/IoT Data and Machine Data go stronly to Time Series technologies
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Analytics
Asset #1:Geo TimeSeries®
+
A disruptive architecture for sensor data in which all data are not only defined by time, but by time+location
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Analytics
Asset #2:Analytics &Language
800 functions
A efficient framework for applications developers - To go faster and to focus on their core business first- To improve radically performances
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
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
Total control guaranteed by WarpScript language > Key differentiator
7Machine Data
Analytics
Businessapplicationsandservices
Datacollection- Communications
GenericLayout
Warp10
WarpScript
Warp10 Data Storage or connectors to existing database
Library of functions, algorithms & tools +Language
Interactive modules
VisualizationReal timestreaming
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Analytics
Performances&scalability
Ingestion, data management and analytics- Real time operations- Total scalability
Ingestion from 100.000 measures per second to 1.5 million per core
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Machine learning tasks:- Sensors & Multisensors machine
behavior understanding- Classification, regression,
clustering- Anomaly, failure, fault detection- Data visualization
Tree-based Algorithms- Random forests- Gradient boosted trees
Deep learning- Artificial neural networks
(perceptron, convolutional, recurrent, long short term memory, ...)
- Generative adversarial networks
Statistical Learning- Support vector machine- Bayesian Networks- Stochastic control/Markov
decision processesDimensionality reduction- t-distributed stochastic neighbor
embedding (t-SNE)- Principal component analysis- Independent component analysis- Laplacian eigenmaps- Isomap
Warpscriptproposesgenericextensionstolearn,toreproduce,todetect,topredict,ortosimulate
patterns,behaviors,outliers,anomalies,failures…
FromBigDatatoArtificialIntelligence…orHowtotakeprofitofcomplex algorithms inbusiness applications
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Analytics
3offers
Standalone Version
DistributedVersion
Embedded Version
HA
Datalog
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Analytics
OpenSourceDistribution
http://www.warp10.io
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Analytics
Ranked #5
Warp10designed byMathiasHerberts,Co-founder, CTO
Warp10
Warp10 Use
Cases#2
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Analytics
UseCase#1:Energyonthewholevaluechain
Transmission Microphasers
Ingesting Renewable energies from small sources to bigger ones in real time
Market / trading
Industrial Consumption
Cities, Neighborhoods, Buildings
Storage
Home Consumption
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Analytics
UseCase#2:SmartCity/Mobility
Future mobility is typically the use case that requires to cross a large range of data.With CIRB in Brussels, Cityzen Data aims to :• To build up horizontal and scalable data infrastructure• To analyze data in order to improve traffic management and to propose services to citizens
Multimodal mobility management based on gathering and crossing data from:- Public transport timetables- Public transport real time data- Traffic monitoring- Traffic lights control- Trackers- Weather forecasts- Air quality- Electric vehicles stations monitoring- Bike sharing station monitoring- Car parks- Smartphones- Roads & streets works status …
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Analytics
UseCase#3:SmartBuilding/SmartFactories
Smart Building and Smart Factories are controlled by a large range of sensors in different areas: electricity, lighting, heating, water, cooling, security ….
Storing and crossing data allow to get value from a large amount of existing data
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Analytics
Source:Airbus
UseCase#4:Aeronautic maintenance
3 years data of a operational aircraft fleet analyzed by “off-the-shelf” warpscript functions
Speed
Alti
tude
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Analytics
A Self-Driving Car Will Create 1 Gigabyte of Data Per Second
Last year, an estimated 26 million connected cars collected more than 480 Terabytes of data. That number is expected to increase to 11.1 petabytes by 2020. Some plug-in hybrid vehicles are capable of generating up to 25 Gigabytes of data in just one hour.
UseCase#5:ConnectedCars
Sources:http://www.economist.com/blogs/economist-explains/2013/04/economist-explains-how-self-driving-car-works-driverlesshttp://www.ibmbigdatahub.com/blog/how-vehicle-telematics-changing-auto-industryhttps://datafloq.com/read/self-driving-cars-create-2-petabytes-data-annually/172
Warp 10 value proposition based on dual data analytics : embedded
analytics in car + cloud
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Analytics
UseCase#6:Groupama SailingTeam
Data analytics:- To improve performances
- To predict problems
Cityzen Data assists French Team for America’s CupBermuda 2017
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Analytics
UseCase#7:Systems,IT,TelecomMonitoring
Warp 10 cross and analyzes data coming from any type of sensors, subsystems, scada… to detect fault, anomalies, discords or to predict future troubles
Monitoring of systems is done by sensors measuring a large
range of parameters: mechanic, power, data
transmission, data processing, latency …
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Analytics
UseCase#8:Majorgroupinvestingworldwideinagriculturecompanies(2017)
Cityzen Data assists a worldwide company to prevent risks on the yields and market value of production
Warp 10 leveraged to cross and analyze :- Data coming from local sensors
- Data generated by equipment (manure spreaders, irrigation …)
- External data like weather forecast
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Analytics
UseCase#9:Health,wellnessandsport
The original use case for Cityzen Data : getting data from smart textile in order :- To understand body behavior - To detect historical and personal patterns- To anticipate specific troubles or diseases- …
MovementsHeart rate / ECGTemperaturePH
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Analytics
UseCase#10:Security/Cybersecurity
Security and cybersecurity need to cross data from a large range of sources.
Geo Time Series™ technologies is particularly relevant to detect weak signals among the ocean of various types of data.
The Warp10 technology is used to monitor Internet accesses.
Internet accessInternet surfingSmartphonesSocial mediaPersonal connectionsPhone callsMailsPaymentsHard disks contentsBorders controlsAirport controlsInterview minutesVideosecurity
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Analytics
UseCase#11:Conditional&PredictiveMaintenance
To get real value in maintenance, sensors data need:
To be synchronized
To be historicized
To be cleaned
To be modeled
To be crossed with other data
To be interpreted in real time (and in batch mode)
Cityzen Data provides all these features through “off-the-shelf” tools & algorithms to different sectors: industry, power generators, cars, aeronautics, telecoms …
A strong trend: “Where Predictive Analytics Is Having the Biggest Impact”. Harvard Business Review
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Analytics
UseCase#12:Testbeds
Testbeds are facing to the increasing of the number of physical sensors and systems probes. They produces a huge amount of measures.
Warp10 provides “on the shelves” features and tools to testbeds experts to get benefit from:- Dealing with a large amount of data- Cleaning and synchronizing high frequency data- Detecting weak signals- Taking profit of data historian
Beyond the tech, a new paradigm in IT
architectures#3
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Analytics
Legacydatatechnologiesarenotadaptedtonewdatachallenges
Dataare« owned »byabusinessorspecificentity
1- OwnershipDatarefertoaspecificformatandontology
2- Interoperability
Thirdapplicationsdecreaseprimaryapplicationsperformances
3- Loadbalancing&performances
« Nonrelevant »dataareremovedbyprimaryapplications
4- MissingInformation
Difficulttofacetonewandunknownfuturesourcesofdata
8- Nonflexibilityinfuture
BIrequestheavyextractionoperation(Datawarehouse)
5- Rigidextractions
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Warp10technologyaddressesallthesechallenges
Dataclosetorowdataareeasiertobemanagedregardingownership
1- Ownership
Timeseriesdataaredefinedbya universalformat
2- Interoperability
« Timeseries »architecturesarescalable
3- Loadbalancing&performances Alldata– evenmistakes–arestored
4- Nomissinginformation
Naturalingestionofnewsourcesofdatawithoutanyimpact
6- Flexibilityforfuture
Dynamicaccessestoselecteddata
5- Ontheflyaccesses
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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
30Machine Data
Analytics
IamcomingoutfromaWarp10@cityzendatapres…Iamfeeling likeHowardCarteropeningTutankhamuntomb…itwillnolongerbethesame
