Xanadu for Big Data + IoT + Deep Learning + Cloud Integration Strategy

©2017 Xanadu Big Data, LLC All Rights Reserved www.xanadubigdata.com
Xanadu for
Big Data + IoT + Deep Learning + Cloud
Integration Strategy
July 25, 2017
Alex G. Lee (alexglee@xanadubigdata.com)

©2017 Xanadu Big Data, LLC All Rights Reserved
Index
-Big Data in IoT & Deep Learning
-Challenges of IoT Big Data Analytics Applications
-Challenges of Cloud-based IoT Platform
-Cloud-based IoT Platform Use Case: GE Predix for Smart Building
Energy Management
-Fog/Edge Computing & Micro Data Centers
-Deep Learning for IoT Big Data Analytics Introduction
-Deep Learning for IoT Big Data Analytics Use Case
-Distributed Deep Learning
-Big Data + IoT + Cloud + Deep Learning Insights from Patents
-Big Data + IoT + Cloud + Deep Learning Strategy Development
-Designing Data-Intensive Applications
-Xanadu Functionality
-Xanadu Use Case
-Xanadu + Deep Learning + Hadoop Integration

Big Data in IoT & Deep Learning
Big Data IoT Deep Learning
Volume:
Size of Data
Very large volumes of
data from sensors and
connected devices
Increase in algorithm
complexity and
computing time
Velocity:
Data Processing Speed
Real time streaming data
of very shot time scales,
high frequencies, high
ingestion rates
Difficulty in data learning
process in a timely
manner
Variety:
Different Types of Data
Heterogeneous datasets
from geographically
distributed diverse IoT
sensors and connected
devices
Increase in data
processing diversity for
different data
characteristics and
behavior
Veracity:
Truthfulness of Data
Noisy & incomplete data
which are characterized
by uncertainty; Data
security issues
Significant increase in
pre-processing of data

How to provide interoperability among heterogenious IoT data streams in
formats, velocities, and semantics?
How to provide data reliability by taking into account noisy and
incomplete nature of IoT data streams?
How to process nearly real-time streaming IoT data?
How to deal with temporal and special dependences of IoT data?
How to comply with security and privacy requirements of IoT data?
Challenges of IoT Big Data Analytics Applications

A cloud-based IoT platform should provide a dynamic and flexible
analytics resource sharing platform delivering IaaS, PaaS, and SaaS.
A cloud-based IoT platform should provide optimized data management
and processing across multiple geographically distributed datacenters.
A cloud-based IoT platform should provide required quality of service
guarantees regarding data storage management and security and
privacy of sensitive data.
A cloud-based IoT platform should provide low latency end-to-end
processing of IoT data.
Challenges of Cloud-based IoT Platform

Cloud-based IoT Platform Use Case:
GE Predix for Smart Building Energy Management
GE Current demo@GE Predix Boston (Industrial Internet) Meetup

Fog/Edge computing extends the cloud computing and services to near
the IoT sensors and connected devices.
Fog/Edge computing is similar to the cloud computing in data storage and
management, data processing and application services, but significantly
different in terms of its short geographical distance from edgy devices,
its dense geographical distribution, and its support for mobility.
Fog/Edge computing requires micro data centers that are scalable,
flexible and agile data centers co-located with distributed IoT data
sources.
Fog/Edge Computing & Micro Data Centers

Deep Learning for IoT Big Data Analytics Introduction
Convolutional Neural Networks (CNNs): image/video analysis
Source: Deep Learning A-Z™: Hands-On Artificial Neural Networks by SuperDataScience@udemy
Recurrent Neural Networks (RNNs):time series analysis
Reinforcement Learning (RL): automatically determine the ideal behaviour
within a specific context, in order to maximize its performance for a specific goal
Source: Reinforcement Learning in Python by Lazy Programmer Inc.@udemy

Deep Learning for IoT Big Data Analytics Use Case
CNN + RNN
Source: SoftPoint Consultores S.L.
Deep RL
Source: Mobileye
Source: ODSTCEAST 2017

Distributed Deep Learning
Issues with IoT big data in deep learning:
High resolution/scale microscopic images 81,025 pixels by 86,273 pixels
(roughly 6.99 gigapixels) requires 78.12 GB memory to store
Trained on 1.28 M images and evaluated on 50 K images took 2 – 3
weeks using 8 GPU machine in MS ResNet
Distributed training:
In model parallelism, different machines in the distributed system are
responsible for the computations in different parts of a single network - for
example, each layer in the neural network may be assigned to a different
machine.
In data parallelism, different machines have a complete copy of the model;
each machine simply gets a different portion of the data, and results from
each are somehow combined.
For details: Skymind web: http://engineering.skymind.io

US20170175645 (GE: IIoT Application)
Deep learning enables automatically learning actionable information
relevant to a desired operation of a gas turbine in industrial plants from
seemingly uncorrelated massive amounts of sensor and controller data.
Big Data + IoT + Cloud + Deep Learning Insights from Patents

US20170169358 (Samsung: Fog Computing)
Decentralized deep learning system on the local IoT data using edge
storage network that is configure to store and process streaming IoT data.

US20170060574 (Foghorn Systems: IIoT + Fog Computing)
A real-time edge IoT analytics system that can handle the large amounts
of data generated by industrial machines and provides intelligent edge
computing platform.

US20160196527 (FALKONRY : Smart Logistics)
Cyber-physical supply chain logistics transportation system for predictive
estimation of QoS across supply chains using condition monitoring and
predictive analytics.

Source: Prof. Pierre Azoulay@MIT Sloan Executive Education
Big Data + IoT + Cloud + Deep Learning Strategy Development

Chapter 2 Data Models
Chapter 3 Storage and Retrieval
Chapter 5 Replication
Chapter 6 Partitioning
Chapter 7 Transactions
Chapter 8 Trouble with Distributed Systems
Chapter 9 Consistency and Consensus
Chapter 10 Batch Processing
Chapter 11 Stream Processing
Designing Data-Intensive Applications

Xanadu enables competitive big data management in the Clouds or
Enterprises
Xanadu Functionality
K
AnyTypes&SizeofData
NoSQL Database
Data De-duplication
Data Replication
Massively Scalable Fault Tolerance
Data Store
NoSQL
Database
ACID Compliance
High
Throughput
Low Latency
Data
Access

Xanadu Performance BMT

Xanadu Fault Tolerance Test Demo

Xanadu provides a composable architecture that can be integrated with
other big data systems
Xanadu Use Case
+
Total Integration
Big Data
Applications
Big Data
IT Infrastructure
Xanadu
Data Management
Platform
GPS /
GLONASS
WCDMA /
LTE
+

Xanadu Commodity Storage System Use Case

Xanadu Cloud Computing Use Case

AlexNet CNN architecture in
DeepLearning4J (DL4J)
Distributed deep leaning on
CPUs/GPUs (+Hadoop/Spark)
Xanadu file system used to store
images and load directly into DL4J
Local Machines & AWS Clusters
Xanadu + Deep Learning + Hadoop Integration
Source: researchgate.net.
Data Source: https://www.kaggle.com/c/diabetic-retinopathy-detection

Thank you
Xanadu Big Data, LLC

Xanadu for Big Data + IoT + Deep Learning + Cloud Integration Strategy

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