Warp 10: the key enabler of digital transformation of IoT business - - PowerPoint PPT Presentation

warp 10 the key enabler of digital transformation of iot
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Warp 10: the key enabler of digital transformation of IoT business - - PowerPoint PPT Presentation

Warp10 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


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Warp 10: the key enabler of digital transformation

  • f 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 Data www.cityzendata.com / www.warp10.io

Warp10

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Warp 10: A powerful and disruptive software platform to address sensors / IoT / Machine Data

#1

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Machine Data Analytics

Four generic families of Data

#1

Data from major companies databases

Big Data = Data Mining No Disruption

#2

Data from Social Networks, mails …

Semantic, Content analytics

Relational SQL NoSQL

#3

Data for Sensors, Meters, Mobiles …

Where IoT Big Data is

NoSQL Time Series

#4

Geospatial databases

Structured Géospatial 3D Modeling

Iot Data = Machine Data = Time Series

examples When traditional IT application is based on relational databases, IoT requires other tech and more flexible approaches

Warp10

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Machine Data Analytics

Warp 10/ Asset #1: Geo Time Series™

+

A disruptive architecture for sensor data

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Machine Data Analytics

Warp 10/ Asset #2: Analytics & Language

800 functions

A efficient framework for IoT applications to develop faster and to focus on their core business first

From summary statistics to advanced signal processing and pattern detection

A stack based language dedicated to time series analytics

Things / Sensors Data transmission Data cleansing Data synchronisation Statistics, pattern detection, machine learning, correlations, anomalies detection … Data filtering Business Application Predictive

Warp10

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Machine Data Analytics

Warp 10/ Asset #3: High Level Security

Secured by Design

Metadata encrypted by default Total data encryption in option Dynamic allocation of cryptographic tokens Data access by token holders tracked Data portfolio management

Total control guaranteed by WarpScript language > Key differentiator

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Machine Data Analytics

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 processes

Dimensionality reduction

  • t-distributed stochastic neighbor

embedding (t-SNE)

  • Principal component analysis
  • Independent component analysis
  • Laplacian eigenmaps
  • Isomap

Warpscript proposes generic extensions to learn, to reproduce, to detect, to predict, or to simulate patterns, behaviors, outliers, anomalies, failures …

From Big Data to Artificial Intelligence

…or How to take profit of complex algorithms in business applications

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Machine Data Analytics

3 offers

Standalone Version Distributed Version Embedded Version

HA Datalog

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Machine Data Analytics

Open Source Distribution

http://www.warp10.io

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Machine Data Analytics

Cloud or On Premise IT Telecoms To an horizontal business value added for apps Sensors Meters IoT

  • - - - - - DATA -- - - - -

Energy App Transport App Health App

Warp10 by Cityzen Data

A 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 data

Cityzen Data is a tech companies without any interference with client

  • wn business.

Businesses functions, algorithms and services developed by clients remain their property.

100,000 to 1.5 million measures / sec / Core

A key value booster for getting value from IoT & events data

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Machine Data Analytics

Ranked #5

Warp10 designed by Mathias Herberts, Co-founder, CTO

Warp10

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Warp10 Use Cases #2

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Machine Data Analytics

Use Case #1: Energy on the whole value chain

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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Machine Data Analytics

Use Case #2: Smart City / 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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Machine Data Analytics

Use Case #3: Smart Building/Smart Factories

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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Machine Data Analytics

Source: Airbus

Use Case #4: Aeronautic maintenance

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Machine Data Analytics

A Self-DrivingCar Will Create 1 Gigabyte of Data Per Second

https://datafloq.com/read/self- driving-cars-create-2-petabytes- data-annually/172

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

  • f generating up to 25 Gigabytes
  • f data in just one hour.

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

Use Case #5: Connected Cars

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Machine Data Analytics

Use Case #6: Groupama Sailing Team

Data analytics:

  • To improve performances
  • To predict problems

America’s Cup Bermuda 2017

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Machine Data Analytics

Use Case #7: Systems, IT, Telecom Monitoring

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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Machine Data Analytics

Use Case #8: Major group investing worldwide in agriculture companies (2017)

Data Analytics 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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Machine Data Analytics

Use Case #9: Health, wellness and sport

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

Movements Heart rate / ECG Temperature PH

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Machine Data Analytics

Use Case #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 access Internet surfing Smartphones Social media Personal connections Phone calls Mails Payments Hard disks contents Borders controls Airport controls Interview minutes Videosecurity

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Machine Data Analytics

Use Case #11: Conditional & Predictive Maintenance

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 “on the shelves” 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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Machine Data Analytics

Use Case #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
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Beyond the tech, a new paradigm in IT architectures #3

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Machine Data Analytics

From transactions to events streams

Business Process Assets HR Devices Sensors Systems IT architecture driven by transactions Rigid business applications & databases IoT Sensors Events Transactions IT architecture driven by stream

  • f events

Flexible and scalable architecture Operations

Cityzen Data

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Machine Data Analytics

Legacy data technologies are not adapted to new data challenges

Data are « owned » by a business or specific entity 1- Ownership Data refer to a specific format and ontology 2- Interoperability Third applications decrease primary applications performances 3- Load balancing « Non relevant » data are removed by primary applications 4- Missing Information SQL technologies have weak performance for IoT Data 5 - Performances Difficult to face to new and unknown future sources of data 8- Non flexibility Data management tied to vendors applications 7- Non independency SQL BI request heavy extraction operation (Datawarehouse) 6- Rigid extractions

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Machine Data Analytics

Warp 10 technology addresses all these challenges

Data close to row data are easier to be managed regarding ownership 1- Ownership Time series data are defined by a universal format 2- Interoperability « Time series » architectures are scalable 3- Load computing All data – even mistakes – are stored 4- Missing information Hig level performances for IoT Data (x10, x100) 5 - Performances Natural ingestion of new sources of data without any impact 8- Flexibility Neutral architecture regarding applications 7- Independency Dynamic accesses to selected data 6- On the fly accesses

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Machine Data Analytics

Time Series: the only realistic and efficient way to break the silos

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

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06/12/2016 Cityzen Data

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I am coming out from a Warp 10 @cityzendata pres … I am feeling like Howard Carter

  • pening Tutankhamun tomb …

it will no longer be the same

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Mathias Herberts – CTO mathias.herberts@cityzendata.com Hervé Rannou – CEO herve.rannou@cityzendata.com

Warp10