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June 28–30, 2016São Paulo
JavaOneSES12228 Implementing “Fast and FuriouFastData and Cloud Platforms God’s Eye” with Oracle’sitle
Andrea AlvarezSolution SpecialistOracle Costa Rica
Thomas MaragnoSales ConsultantOracle Caribbean
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What is the “God’s Eye”?
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What is the “God’s Eye”?
• A pen drive?• A SD Card?
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What is the “God’s Eye”?
• Piece of code created by Ramsey
• “Is able to hack any type of technology.”
• Can take control of their camera
• Returns the information gathered in less than 4 min
• Can locate any given person anywhere in the world.
• It can be taken over by anyone
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Demo
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Demo
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High Level Steps
Detected facesand crop image
Live Feed
Process facesand try to recognize
the subject
Correlate datawith static known
data
Consume correlated data from queueShow live feed
1
2a
2b3
JMS Q JMS Q
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Edge Analytics
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Used Components
Compute CS/DevCS JCS/DevCS
Stream Analytics
Database CS POI DB
JET AppVia JMSVia JMS
SE Embedded
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Oracle Edge Analytics
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“Oracle OEA (formerly known as Event Processing) is a Java serverfor the development of high-performance event drivenapplications on embedded devices. It is a lightweight Javaapplication container based on Equinox OSGi, with sharedservices, including the Oracle CEP Service Engine, which provides arich, declarative environment based on Oracle Continuous QueryLanguage (Oracle CQL).”
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Adapter Channel
Processor/Query Event
1 Face DetectionConcepts
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1 Face Detection
CEP Engine
Query
Query
Inp
ut
Ad
apte
rs
Ou
tpu
t A
dap
ters
event
event
event
Real-time event data Context-aware filtering, correlation, aggregation and processing of data
Processed business events for downstream applications
event
event
eventSensors
Backend Applications
High Level Architecture
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1. Face Detection
Tap into the camera
Steps
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1 Face Detection• Implements RunnableBean
and StreamSource Interfaces
• Convert data coming from physical devices to OEA Events
• Convert OEA Events
• Future Plans: Replace with IoTCS
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Video Adapter
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1. Face Detection
Send the live feed downstream.
Steps
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1. Face Detection
Clean events. Discard errors and duplicates
Steps
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1. Face Detection• Implements RunnableBean
and StreamSource Interfaces
• Convert data coming from physical devices to OEA Events
• Convert OEA Events
• Future Plans: Replace with IoTCS
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CQL / Processor
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1. Face Detection
Destination: JMS Q
Steps
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1. Face Detection• JMS Adapter not implemented
in Edge Analytics
• Custom JMS Adapter built
• Implements StreamSinkInterface
• Convert OEA Event to MapMessage
• Future Plans: Replace with IoTCS
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Custom JMS Adapter
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JMS Q JMS Q
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High Level Steps
Detected facesand crop image
Live Feed
Process facesand try to recognize
the subject
Correlate datawith static known
data
Consume correlated data from queueShow live feed
1
2a
2b3
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Edge Analytics
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Used Components
Compute CS/DevCS JCS/DevCS
Stream Analytics
Database CS POI DB
JET AppVia JMSVia JMS
SE Embedded
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2a. Face Recognition
Standard OOTB pre built JMS Adapter
Steps
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2a and 2b. Face Recognition and Correlation
Actual face recognition occurs here
Steps
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“OpenCV is one of the most popular libraries used to develop ComputerVision applications. It enables us to run many different Computer Visionalgorithms in real time. It has been around for many years, and it hasbecome the standard library in this field. One of the main advantages ofOpenCV is that it is highly optimized and available on almost all theplatforms.”
Authors of OpenCV By ExamplePrateek Joshi; David Millán Escrivá; Vinícius Godoy
What is OpenCV
2a and 2b. Face Recognition and Correlation
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2a and 2b. Face Recognition and Correlation
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Detecting objects and or Recognizing faces – implemented process
Haarcascades
Training image set
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2 Face Recognition
Discard low confidence recognitions
Steps
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2 Face Recognition
Send downstream (JMS) for JET App Consumption
Steps
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2 Face Recognition
Testing purpose. Write event to CSV
Steps
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JMS Q JMS Q
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High Level Steps
Detected facesand crop image
Live Feed
Process facesand try to recognize
the subject
Correlate datawith static known
data
Consume correlated data from queueShow live feed
1
2a
2b3
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Edge Analytics
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Used Components
Compute CS/DevCS JCS/DevCS
Stream Analytics
Database CS POI DB
JET AppVia JMSVia JMS
SE Embedded
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3 Consume Messages from JET
• Collection of Open Source and Oracle-built JavaScript Libraries
• Rich Component Set with Oracle Data Visualization
• Alta Theme Built-in
• Oracle Standards Compliant
• Security.
• Common Model API.
• Unique features for mobile apps development.
• Internationalization.
JET JQuery JQueryUI Knockout RequireJS Hammer
What is JET?
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3 Consume Messages from JET
• Use of Model-View-ViewModel (MVVM) architecture– Better organized code by isolating logic for managing a specific part of the UI
– Natural point for plugging in View Unit Tests
– Enables automatic View updates in response to data changes and user input, reducing the quantity of business logic (or glue) required in the code
– Enables designer-developer workflow due to loose coupling between View markup & code behind
• Use two-way data binding to ease development
38
What is JET?
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Oracle JET for FaceRecon app
39
Users
RESTADF Backend
JMS Queue
Server Side
Client Browser
JET Common Model
HTTP/JSON
Google Maps Third-party Cloud API
Mobile/Web FaceReconApplication
JCS/DevCS
Business Services
Data
Model
Knockout.JS ViewModel
View
Re
qu
ireJS
JET UI Components
JQueryUI Hammer Alta UI Theme
JET rou
ting
system
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Future Plans
Detected facesand crop image
Live Feed
Process facesand try to recognize
the subject
Correlate datawith static known
data
Consume correlated data from queueShow live feed
Store videoSegments in hadoop
for analysis
JMS Q JMS Q
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Unlocking Value of IoT Data via Analytics & Integration
Time
Bu
sin
ess
Val
ue
Act Now
HOTSchedule
Maintenance
WARMPlan for
next release
COLD
42
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Working with Big Data: some challenges
1. Big data ≠ Infinite storageYes, storage is cheap but it helps to have clean data, with context and less redundancy
2. Hadoop is batch-oriented and there is inherent latency "With the paths that go through Hadoop [at Yahoo!], the latency is about fifteen minutes […] it will never be true real-time. " *Raymie Stata, Yahoo! CTO(June 2011)
*: http://www.theregister.co.uk/2011/06/30/yahoo_hadoop_and_realtime/
minutes
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Get ahead of the curve
1. Filter out noise (ex: data ticks with no change), add context (by correlating multiple sources), increase relevance
2. Identify certain critical conditions as you insert data in warehouse
Move time-critical analysis to front of process
Filter out, correlate
Use Event Processing techniques
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Getting Ahead of the Curve
Fast Data
Big Data
minutesms
Fast Data
His
tori
cal d
epth
: dee
p
His
tori
cal d
epth
: sh
allo
w
Example: analysis of traffic patterns and congestion times for urban planning
Example: monitoring of traffic cameras to ensure given license plates are not in use on multiple vehicles Add “depth” to your fast data by merging output
of MapReduce to stream processing
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Use Case: City Automation & Traffic Management
•Automatic number
plate recognition
•Red-light violation
detection
•Traffic jam detection
•Vehicle location
system
•Geo-fences
Source: http://www.flickr.com/photos/epsos/5591761716/
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Use Case: Remote Patient Monitoring
•Data evaluation for
potential health
problems
•Automatic alerts to
caregivers and
healthcare providers
•Historical data analysis
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Use Case: Home Automation
•Real-time pricing with
adaptive intelligence for
carbon footprint
reduction
•Automatic water usage
metering enables
conservation
• Intrusion detection
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Questions?
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