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| 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
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
Today’s agenda
2
▶ From Smart X,
▶ Smart Systems,
▶ Smart Data Integration to
▶ Smart Factory: ScaleIT
▶ Q&A
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
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
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
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
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
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.
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
How to transforms a Thing to Smart Thing?
6
And many other aspects - Identity / Discovery - Security - Lifecylce - Usage data - ...
Communication Communication Communication Communication
Self Mgmt.
„Intelligence“ „Intelligence“
Knowledge Base
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
Smart Things require Smart Data
7
Meta Meta Data
C C C C
C C C C
C C
C C
C C C C
C C C C
Smart Smart Data
• Time • Location • Accuracy • Value range • Vendor • ...
Data
Data describing the context • Process • Order • Lot • ...
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
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
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
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
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
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
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
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.
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
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 models Integration of the conceptual / engineering models
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
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
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
Data Integration Requirements in a Smart World
14
Heterogeneity Heterogeneity
Extensibility Extensibility
Holistic Approach Holistic Approach
Real Time Real Time
Low Effort Low Effort
Data
In
teg
ratio
n Scalability Scalability
Req. Classic DI Req. Classic DI
End User Enabled End User Enabled
Plug & Work
M2M
Predictive Maintenance
Lot 1 Production
Data and Meta Data Data and Meta Data
…
Scenarios / Use Cases Requirements
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
Data Integration for Digitalization in Production
15
Mech. Engineering Mech. Engineering
Shopfloor Managemen
t Managemen
t
Elec. Engineering
IT Engineering Data
Integration
Design Design
Planning Planning
Engineering Engineering
Production Production
Service Service
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
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
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
Data Integration Focus
17
Knowledge
Knowledge
Information Information
Data Data
Meta Data
Meta Data
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
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
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
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, ...
Syntactical Data Integration
•Independence from formats and language
•JSON, CSV, DSLs, AutomationML, ...
Technical Data Integration
•Independence from interfaces and respective technologies
•OPCUA, MQTT, COAP, fieldbusses, ...
Technical Data Integration
•Independence from interfaces and respective technologies
•OPCUA, MQTT, COAP, fieldbusses, ...
IT IT
Data sources on the shopfloor or in IT
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
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
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
Federation Concept
21
Technical Data Integration
OPC UA Ontology
MQTT Ontology
COAP Ontology
Vendor Specific Ontology
• Discovery • Identity Management • Property Management
Syntactical Data Integration
Adapter
MES / ERP
Ontology
| 05-10-2016 | Joachim Hoernle | © Bull/AtoS Bull/AtoS | Bull Software Solutions
The Big Picture
22
Modell Mgmt.
Technical Data Integration
OPC UA Ontology
MQTT Ontologie
COAP Ontology
Vendor Vendor specific
Ontology
Syntactical Data Integration Utility
Ontologies
Mapping Ontologies
Semantic Data Integration
FMEA Ontologie
Engineering Modell
Abstract Abstract Sensor
Ontology
User Ontology
Representation Layer
Testing Ontology
Ontology Ontology encapsulating
a DSL
xxxML Ontology
Concrete Concrete Sensor
Ontology
Security Ontology
Mgmt. Models
Atos, the Atos logo, Atos Consulting, Atos Worldgrid, Worldline, BlueKiwi, Bull, Canopy the Open Cloud Company, Yunano, Zero Email, Zero Email Certified and The Zero Email Company are registered trademarks of the Atos group. February 2015. © 2015 Atos. Confidential information owned by Atos, to be used by the recipient only. This document, or any part of it, may not be reproduced, copied, 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
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