The document discusses the challenges of integrating industrial data from different sources due to its large volume, varied formats and complex relationships. It introduces MIx Core, a machine intelligence solution from Bit Stew Systems that can automate data integration through feature extraction, relationship mapping and generating a semantic model to solve problems that overwhelm traditional ETL approaches. The system uses machine learning algorithms to classify data fields and dynamically understand changing relationships without requiring large, costly integration teams.

1. 1 Bit Stew Systems Inc. Confidential and Proprietary
Solving Industrial Data Integration with
Machine Intelligence
By: Mike Varney
Executive Director, Product Management & Strategic Initiatives at Bit Stew

2. 2 Bit Stew Systems Inc. Confidential and Proprietary
The Problem with Big Data
With 50 billion devices connected to the
Internet by 2020 rapid data proliferation will
continue to choke the progress of data
analytics projects.
Analytics is only the tip of the iceberg. Data
integration, data quality, and challenges in
extracting value lurk beneath the surface
and are amplified by the growth of
Industrial data from IIoT.
Analytics are the tip
of the iceberg
Data cleansing
and quality
challenges lurk
beneath the
surface

3. 3 Bit Stew Systems Inc. Confidential and Proprietary
Two Different Worlds, One Common Problem
Operational Technology
(OT)
Information Technology
(IT)
Industrial IoT
• Asset Performance
• Real-Time Status Monitoring
• Diagnostics & Service
• Predictive Maintenance
• Software Updates
• Data Integration
• Data Cleansing
• Systems Management
• Analytics
• Value-Added Services
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Hidden Cost of Integration
• Industrial data is not easy
• Complex data, variable size, frequency, and format
• Disparate sources
• No view of data relationships across the enterprise
• Often rely on traditional ETL or BI tools
• Large integration teams costing thousands of dollars

5. 5 Bit Stew Systems Inc. Confidential and Proprietary
Complex Environments Lead to Complex Data
Why is Industrial Data so complicated?
Variety of source types Messy Data Complex Data
Relations
Different Sizes Massive Volume Varied Frequency

6. 6 Bit Stew Systems Inc. Confidential and Proprietary
Traditional ETL Compounds the Problem
This model does not work for large
volumes of complex OT and IT data
Extracts source data
without contextual
understanding
Then a team of
integrators, architects,
and data scientists apply
transformations
After which, finally
it can be provided
to business and
operational teams

7. 7 Bit Stew Systems Inc. Confidential and Proprietary
Why MIx Core?
Small teams can solve BIG industrial problems with MIx Core™
• No longer need large and costly integration teams
• IoT endpoints are being added at an accelerated pace that is
unmanageable by human computing
• Machine Intelligence solves the compounding data problem
in Industrial environments

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Using Machine Intelligence to Automate Data Integration
• Comprehensive Semantic Model
• Automatic sensing of data and mapping to Semantic Model
• Dynamic and adaptive to source data
• Examine, transform and clean data
Machine
Intelligence
Create Semantic Model
Siloed Data Sources
Intelligent Integration to
Target Model
• Integrate with APIs, services, files, streams and
network traffic
• Embedded guaranteed messaging
• Interactive UI for viewing and editing

9. 9 Bit Stew Systems Inc. Confidential and Proprietary
Step 1: Create Feature Vector
– Source data is read and sent to the Hierarchical Feature
Extractor
– The Hierarchical Feature Extractor produces Feature Vectors
– The Hierarchical Feature Extractor can be customized for
domain specific feature extraction and machine learning.
– Feature vectors are used to fingerprint and classify the input
data.
Step 2: Store denormalized data into the Data Index
– The Feature Vectors are stored into the Data Index
– Data is denormalized and fed into the Relationship Association
– A Relationship Matrix is created and stored into the Data Index
How it Works?

10. 10 Bit Stew Systems Inc. Confidential and Proprietary
How it Works?
Step 3: Create Semantic Model
– The Feature Vectors are sent to the Field Classifier
– The Field Classifier uses supervised and unsupervised machine
learning algorithms to relate and map data
– Each algorithm gets a vote to determine the best model
– The related and mapped data is sent to the Modeler to create a
Semantic Model
Step 4: Publish the Semantic Model to Target Systems
– Model is published and available for use in the platform
– The semantic model may be exported to 3rd party systems for
further analysis
– Changes to published model are tracked over time for the system
to learn how relationships in the data change over time

11. 11 Bit Stew Systems Inc. Confidential and Proprietary
Learn more about Bit Stew
Visit www.info.bitstew.com
Follow us on
Download the whitepaper: http://info.bitstew.com/whitepaper-mike-machine-intelligence

12. 12 Bit Stew Systems Inc. Confidential and Proprietary
Mike Varney spent over 20 years in the US Navy, where his experience
included commanding the most advanced nuclear-powered submarines
in complex operations around the globe, leading a special operations
team in reconstruction efforts in Afghanistan, and directing a Naval
Operations Centre. He has also served as a Strategic Advisor for the US
Department of Defense, a Senior Evaluation Officer at nuclear power
plants, and an advisor to companies providing smart grid technologies to
the energy industry.
Mike holds Bachelor of Science degrees in Nuclear and Marine
Engineering as well as Master of Science degrees in Engineering
Management and National Security Strategy. Today, Mike is the Executive
Director, Product Management & Strategic Initiatives, where he leads the
strategy for Bit Stew Systems MIx Core platform, MIx Developer Network
and Bit Stew University.
About the Author