‘Dynamic schema’, what’s in it for manufacturers? This session will give you a brief overview about the process industries’ IT landscape and how the sector dealt with Big Data long before the term was even invented. We shed a quick light on the evolution of data interface standards such as OPC UA. Taking a quick look into the motivation (and ongoing struggle) for seamless real-time system integration between Control and ERP, this talk will conclude with hard facts on why MongoDB qualifies so well in the industrial solution context. Deprecating the closed-shop data silo, inmation will share first-hand experience from MongoDB-centric system development.

1. MongoDB as a universal data store for
Process Data
Munich
2013

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The talk in brief
It is all about two technology standards (coming together)…

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Timo Klingenmeier
Co-founder, GM and Technical Lead inmation.com
timo.klingenmeier@inmation.com
89-93
EMP (Engineering Company)
Software Developer
93-03
GM, Founder iDV/best solutions AG
Industrial System Integration, Custom SW Development
03-10
GM, Matrikon Deutschland AG
Industrial Software Specialist, Real-Time Connectivity,
Alarm Management, Performance Monitoring
11/12
Consultant
Offshore Wind Projects
13
inmation.com
NextGen Information Management Software

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2013

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Realtime
1ms – 1sec

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ERP / B2B / B2C
MES and PIMS
Control & Automation

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MES
and
PIMS
ACRONYM ALERT!
MES = Manufacturing Execution System
PIMS = Process Information Management System

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1
Refining Company
15
Sites
2000
Applications
CAPEX
OPEX (Maintenance Contracts, etc.)

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2013
Control
Typical Production Process Applications
Advanced Process Control
Mass Balancing
Laboratory Information
Management
Production Scheduling
Alarm Management
Quality Management
Operational
Excellence
Performance, Reliability, Safety
Management
ERP
Continuous Improvement
(Generic Data-Mining)

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The evolution of
industrial system integration
(a brief history of time)

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Industry 3.0
Emptying the parking lots
Source: flickr.com/acarlos1000

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Today
Distributed
Control Systems
Entire Sites
Countless Items
and Actors

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The Spinal Cord in Production:
Contextualized Time-Series
historization of numerical data
and process events.

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2013
The easiest way to
improve a prediction is
to add data. You can’t
infer without data. So,
store the data now and
analyze it later,…[]
Dwight Merriman, MongoDB evangelist

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The
Babylonian
Era

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Protocol Differences
Information
Contextualization
Bandwidth and
Throughput
Implementation
Complexity
Robustness

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Birth of a standard

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You can talk
DCOM to me.
I will present you a qualified and more or
less structured namespace, consisting of
symbolically named items (tags).
Each item may have additional properties.
I can give you the actual value, the
milisecond accuracy and the value quality.

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2013
You can talk
DCOM to me.
On request, I will constantly deliver new
Alarms & Events of any kind. Depending on
the subordinated control systems I am
connected to, the detail content of a single
event record may vary.
I can either supply all or filtered events.

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2013
You can talk
DCOM to me.
Similar to my DA colleague on the left, I
maintain a structured namespace of tags.
Unlike this guy, I know about the entire
history of their values.
On request I will return raw values and
statistical aggregates for any period of
time.

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2013
You can talk SOAP / XML to me.
I can do what the DA guy does, but a little
simplified.

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2013
You can talk either binary TCP or XML to me.
I offer various options for secure communications.
I have many different profiles and facets.
(Some people are confused about me)
My smallest incarnation can work in a single chip
solution, while I’m still qualifying for an enterprise-wide
service. Obviously – Unified Architecture – I can supply
all services of those little guys in one.

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You can talk to me.
(Everybody knows that)
Very similar to the SQL language, I’m not
young of age but definitely not willing to
retire!

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One standard – many faces

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Globally standardized
Universal Real-time
Data Access
Unknown to broader
audience

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Industrial IT Data Storage
Strategies Today
Proprietary High-Frequency
Time-Series Data Formats
SQL
Databases

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Application Differences
Supported
Interfaces
Data Scope
Extraction
Data Format
Storage
Interfaces to
next level

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Too
many
data silos

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Data Reduction & Loss

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How can industries create
affordable, maintainable, open
data stores which allow for the
“Merriman paradigm” in the
specific context of industrial data
mining requirements ?

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the
approach

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The problem to solve

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OS Platform? Scalability? Make your choice!
...or simply grow as required

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How we use MongoDB in our product
12 months ago, there were SQL parts
There was homegrown data serialization
Today, we only use MongoDB for any kind of
data storage
All network transports use inner BSON
chunks, extended for efficient real-time
object communication

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BSON
Command Processor
MongoDB
Core
cmd
cmd_errors
inSys
cmd_processed
BSON
Working Model
Working Model
Core Object
Connector Object
BSON
BSON
Connector Object
Endpoint Object
Endpoint Object
Endpoint
Connector

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Remote
OPC UA Server
Endpoint
Remote
XML-DA Server
Endpoint
Remote/Local
OPC DA Server
Endpoint
Remote/Local
OPC A&E Server
Endpoint
Remote /Local
OPC HDA Server
Endpoint
Connector Service

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Remote
OPC UA Server
Endpoint
Remote
XML-DA Server
SPROX Protocol (Single Port TCP)
Secure Prioritized Realtime Object Xchange
Endpoint
Remote/Local
OPC DA Server
Endpoint
Remote/Local
OPC A&E Server
Endpoint
Remote /Local
OPC HDA Server
Endpoint
Connector Service
Core Service

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2013
Remote
OPC UA Server
Endpoint
Remote
XML-DA Server
Schema Design:
• Multiple Databases
• Multiple Collections
Database Design:
• Replication (Redundancy)
• Port TCP)
SPROX Protocol (Single Sharding (Horizontal Scaling)
Secure Prioritized Realtime Object Xchange
Endpoint
Remote/Local
OPC DA Server
Endpoint
Connector Service
MongoDB
Remote/Local
OPC A&E Server
Different OPC Servers
Endpoint
Remote /Local
OPC HDA Server
Endpoint
Mongo BSON Object
Bulk Inserts
Core Service

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Third-Party Stack (API)
Command Application
•
•
•
•
•
•
Realtime Data Access
Component Registration
Historical Data Access
ComponentEvents
Alarms and Configuration
System Health Monitoring
Any MongoDB Driver
C++, C#, Java, …
Any MongoDB Driver
C++, C#, Java, …
Connector Service
Core Service
MongoDB

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DataStudio
•
•
•
•
•
Component Registration
Component Configuration
System Health Monitoring
Item Monitoring
Log Analysis
BOX Protocol (Single Port TCP)
BSON Object Xchange
Connector Service
Core Service
MongoDB

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OPC UA Server
DataProxy
BOX Protocol (Single Port TCP)
BSON Object Xchange
Connector Service
Core Service
MongoDB
BSON
MongoDB

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Using MongoDB-based
Real-time Data
(and a little bit of Google, too)

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Summary – MongoDB qualifies for
process data storage
It has the performance and the scalability options
required to store hi-freq data in huge amounts
Timestamp accuracy is sufficient
Schema (-less) flexibility fits the variant data structures,
originated in (OPC) source systems
Unbeatable value offer (both software and hardware
utilization)
It exposes ‘natural’ data structures which make any kind
of analysis fun
It satisfies IT people and engineers

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Thank you very much for your
attention