Is today’s Information Technology smart enough for a Smart World?

| 05-10-2016 | Joachim Hoernle | © Bull/AtoS
Bull/AtoS | Bull Software Solutions
Is today’s Information
Technology smart enough
for a smart world?
M2M Summit 2016 - Düsseldorf
Joachim Hoernle
Bull BES
Business and
Enterprise
Systems

Today’s agenda
2
▶ From Smart X,
▶ Smart Systems,
▶ Smart Data Integration to
▶ Smart Factory: ScaleIT
▶ Q&A

BES Business Portfolio
3
Focus Areas and Expertise
IT operations, IT operational safety, IoT Management and Data Integration
– Management
– Monitoring
Business Modell
– Off the shelf software solutions
– Custom solutions
– Respective services
• Consulting and
• Implementation projects
• Trainings

Smart Is The New Green
4
Smart Factory Smart Home
Smart Grid
Smart Cities
Smart Material Smart Health
In future literally every - thing will be smart.In future literally every - thing will be smart.
Smart X

Is There Any Smart Definition?
5
▶ There are several definitions of “smart” floating around.
▶ Typically Smart Systems / Objects
– have some sort of intelligence, the ability to learn
and to deal with or understand situations especially
if they are complex, non-standard or problematic.
– some kind of interaction between the smart object or
system and the ambience, environment or physical context.
– are pervasive and ubiquotous.
– things or systems have some kind of
autonomous behavior.

How to transforms a Thing to Smart Thing?
6
And many other aspects
- Identity / Discovery
- Security
- Lifecylce
- Usage data
- ...
CommunicationCommunication CommunicationCommunication
Self Mgmt.
„Intelligence“„Intelligence“
Knowledge
Base

Smart Things require Smart Data
7
MetaMeta
Data
CC CC
CC CC
CC
CC
CC CC
CC CC
SmartSmart
Data
• Time
• Location
• Accuracy
• Value range
• Vendor
• ...
Data
Data describing
the context
• Process
• Order
• Lot
• ...

Smart Systems
8
▶ Smart systems typically consist of diverse components which are related to the basic
capabilities of the system:
• Sensors for signal acquisition
• Actuators that perform or trigger the required action
• Some kind of knowledge base
• Networking to transmitting information and decision and instruction to the
command-and-control unit
• Power Storage and Energy Management
• …
▶ In addition there are some capabilities which are mandatory
– Integration / information integration / data integration
• Low scale integration – addressed by Smart Systems Integration and similar
approaches
• Large scale integration – currently in the clouds
– Management
• Operations management for the smart world
– Monitoring and control, security, identity, network management

Typical Solutions
9
Cloud
Backend
Data Souces
D
C
Solution
1
D
C
Solution
2
D
C
Solution
3
D
BE
Solution
4
D
C
Solution
5
D
C
Solution
6
C
BE
M Model

Classic Data Integration
10
▶ Old hat: Data integration is an established discipline in IT since many years
▶ Classic data integration is an approach which is typical in enterprise IT.
▶ Strong repository and database focus
▶ Objectives
– Ability to cope with complexity and with inconsistencies at various levels
• Reduce the number of i/fs - provide uniform access to data from multiple sources
• Integrated system illusion
– Facilitate re-use
– Ensure interoperability and provide independence from
• data source specific aspects such as interfaces or hardware: technical DI
• specific representation of information: syntactical DI
• from specific schemes: structural DI
• from specific contextual information: semantic DI
▶ Many different approaches, technologies and tools such as e.g.
– EAI – Enterprise Application Integration
– ETL – Extract, Transform, Load
– EII – Enterprise Information Integration
– ESB – Enterprise Service Bus
– MOM – Message Oriented Middleware

Classic Data Integration Issues
11
▶ Classic data integration is often complex, cumbersome and costly. Mainly because of
the complexity people tend to “divide and conquer”
▶ Therefore data integration technologies are often limited to a small subset of data
sources.
▶ Many steps for cleaning, enrichment, matching and fusion of data have to be performed
manually.
▶ Often people do not distinguish between different types of benefits
– Benefits for technology : ease of IS management or creation of IS
– Benefits for end-user: use of concepts and terminologies from the end user domain
▶ The bad news is: there are not many good example for successful integration initiatives
in IT especially if the subject is large, heterogeneous, complex, polymorphic and dynamic
as it is in the “smart world”.
▶ To address the requirements of digitalization initiatives it is not enough to focus on a
subset of aspects of data integration (technical, syntactical, structural or semantic
integration) or to provided powerful but scattered integration approaches or just
technology. Smart system dealing with smart data require an
– holistic, meta data aware and model based data integrative approach
focusing on the end user domain and includes an
– integration architecture and
– provides the appropriate tools and facilities.

Smart Data Integration
12
▶ Smart data integration focusing on digitalization initiatives has additional and
different requirements.
▶ For instance it is important to support and facilitate the collaboration of experts from
different domains e.g. electrical engineering or software engineering. Experts tend to use
different tools, which are well suited for their specific purpose, but usually do not provide
sufficient mechanisms for cooperation with other engineering tools. Especially cross domain
integration is both, critical and problematic.
Integration of the conceptual / engineering modelsIntegration of the conceptual / engineering models

Smart Data Integration
13
▶ In addition to generic requirements related to data integration digitalization initiatives
requires the following data integration capabilities:
– Multi mode modeling
• Support for different models at the conceptual focusing on the same of similar domain
– Consistent and pervasive integration from the shop floor up to the level of the
engineering tools or management
• technical,
• syntactical,
• structural and semantic integration
– Meta Data Management based on a standardized meta model
• Including lifecycle management of models and meta data
– Mapping and binding facilities
– Abstraction, aggregation and enrichment of information
– End user suitability of the modeling tools
• The major focus is the engineering domain not the IT domain

Data Integration Requirements in a Smart World
14
HeterogeneityHeterogeneity
ExtensibilityExtensibility
Holistic ApproachHolistic Approach
Real TimeReal Time
Low EffortLow Effort
DataIntegration
ScalabilityScalability
Req. Classic DIReq. Classic DI
End User EnabledEnd User Enabled
Plug & Work
M2M
Predictive
Maintenance
Lot 1
Production
Data and Meta DataData and Meta Data
…
Scenarios / Use Cases Requirements

Data Integration for Digitalization in Production
15
Mech. EngineeringMech. Engineering
Shopfloor
Managemen
t
Managemen
t
Elec. Engineering
IT Engineering
Data
Integration
DesignDesign
PlanningPlanning
EngineeringEngineering
ProductionProduction
ServiceService

ScaleIT
16
▶ The ScaleIt project is an „Industrie 4.0“ project funded by German government (BMBF).
▶ The focus is to provide an architecture and components of a scaling ICT for increasing
productivity in mechatronics manufacturing.
▶ https://scale-it.org/
▶ Project partner
– Sick AG
– Zeiss 3D AG
– RoodMicrotec GmbH
– Smart HMI GmbH
– Ondics GmbH
– FEINMETALL GmbH
– digiraster GmbH
– Bull / Atos GmbH
– University Stuttgart
– Fraunhofer Institute IAO
– Karlsruhe Institute of Technology
– microTEC Südwest e.V.
▶ Scalability in terms of the number of components or smart systems but also
in terms of technologies, approaches and standards

Data Integration Focus
17
KnowledgeKnowledge
InformationInformation
DataData
Meta Data
Meta Data

From Data Integration to Data-Morphosis
18
Data Acquisition
EAI/ESB
ETL
Syntactic
Integration
XML Technologien
Semantic
Framework
Semantic
Consolidation
Ontologies,
SPARQL, RDF
Semantic
Framework
Data Information Knowledge
Analytics
Rule based
Systems
Industrial
Intelligence
Insights
Semantic Integration
Syntactical Integration
Technical Integration
Holistic
Model
• Semantic
Annotations
• Binding
• Mapping
- Abstraction
- Aggregation
- Enrichment
Meta Data Repository
- Dependencies
- Relationships

Data Integration Architecture
19
Semantic and Structural Data Integration
•Engineering models, standards ...
•Based on ontologies focusing on the end user
domain or standards
Semantic and Structural Data Integration
•Engineering models, standards ...
•Based on ontologies focusing on the end user
domain or standards
Syntactical Data Integration
•Independence from formats and language
•JSON, CSV, DSLs, AutomationML, ...
•Independence from formats and language
•JSON, CSV, DSLs, AutomationML, ...
Technical Data Integration
•Independence from interfaces and respective
technologies
•OPCUA, MQTT, COAP, fieldbusses, ...
•Independence from interfaces and respective
technologies
•OPCUA, MQTT, COAP, fieldbusses, ...
ITIT
Data sources on the shopfloor or in IT

Engineering Model
20
Example:
Conceptual Engineering Model
Technical
Object
References Meta Data
• Unit
• Accuracy
• Location
• ...
• Process
• Organization
• Order
• Customer
• Product
• Product
component
• IT System
• Failure
• …
• Sensor
• Sensor Node
• Machine
• ...
Semantic
Annotation
Semantic
Annotation

Federation Concept
21
OPC UA
Ontology
MQTT
Ontology
COAP
Ontology
Vendor
Specific
Ontology
• Discovery
• Identity Management
• Property Management
Adapter
MES /
ERP
Ontology

The Big Picture
22
Modell
Mgmt.
OPC UA
Ontology
MQTT
Ontologie
COAP
Ontology
VendorVendor
specific
Ontology
Utility
Ontologies
Mapping
Ontologies
Semantic Data Integration
FMEA
Ontologie
Engineering
Modell
AbstractAbstract
Sensor
Ontology
User
Ontology
Representation Layer
Testing
Ontology
OntologyOntology
encapsulating
a DSL
xxxML
Ontology
ConcreteConcrete
Sensor
Ontology
Security
Ontology
Mgmt.
Models

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Confidential information owned by Atos, to be used by the recipient
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circulated and/or distributed nor quoted without prior written approval
from Atos.
01-08-2016
Thanks
For more information please contact:
M+ 49 170 34 26 975
joachim.hoernle@atos.net

Is today’s Information Technology smart enough for a Smart World?

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