The document introduces the Open Services for Lifecycle Collaboration (OSLC) as a necessary framework for integrating various data standards in domains like Application Lifecycle Management (ALM) and Product Lifecycle Management (PLM). It highlights trends such as the increasing reliance on API standards for data integration and the challenges of managing disconnected data sources within organizations. Additionally, it advocates for the adoption of OSLC to enhance data connectivity and promote better collaboration across engineering disciplines.

1. Open Services for Lifecycle Collaboration (OSLC)
Introduction into open standards
for the integration of ALM, PLM, and more
CONTACT Open World 2019
Axel Reichwein, Koneksys
June 27, 2019
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2. Initially: Focus on using data standards
PhD and postdoc focused on
tradeoff studies
Reconfiguring architecture
model to then automatically
sync detailed
discipline-specific models
Applying rule-based design in
engineering
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3. Now: Focus on using API standards
API standards crucial for
scalable data integration
strategy
API standards have longer life
expectancy than data
standards
API standards enable new
enterprise applications
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4. Koneksys Consulting Services
Connecting data using standards
● Custom software development
for organizations and vendors
● Research projects
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5. Status Quo in Information Management
According to David Meza, Head of Knowledge Management at NASA
● Most engineers have to look at 13 different sources to find the information
they are looking for
● 46% of workers can’t find the information about half the time
● 30% of total R&D funds are spent to redo what we’ve already done once before
● 54% of our decisions are made with inconsistent, or incomplete, or
inadequate information
Quote from https://www.youtube.com/watch?v=QEBVoultYJg
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6. Crosscutting Concerns Across Disciplines
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Requirements
Engineering
Design Manufacturing Operation
Traceability
Configuration
management
Change
Management
Reuse

7. 2 Trends increasing need for connected data
IoT
New feedback loops needed to make
sense of recorded operational data
Autonomy
Explosion of number of test scenarios
Need to link experienced auton.
vehicle behavior (e.g. saved in data
lakes) with test scenarios (e.g. saved in
systems engineering applications) to
assess coverage of test scenarios and
overall vehicle safety
Design
Procurement
Manufacturing
Operation
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8. From model silos to connected data
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9. Example: SUV Rollover
Engineering Domains
● Requirement
● Control System Design
● 3D Model
● Test Case
● Simulation Model
● Change Request
9

10. Example Cross-disciplinary Relationships
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Requirement
3D model
Simulation Model
Controller Model
Test Case

11. Engineering Change Request
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Requirement 3D modelSimulation Model
Change
RequestProblem
Test CaseController Model

12. Discipline-specific PLM & ALM Solutions
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Requirement 3D modelSimulation Model
Change
Request
Test CaseController Model
ALM PLM

13. Discipline-specific Change Requests (not ideal!)
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Requirement 3D modelSimulation ModelTest CaseController Model
PLM
Change
RequestALM
Change
Request
ALM PLM

14. Example ALM/PLM Integrations (based on OSLC)
Linking PLM Parts with ALM requirements
Linking PLM Engineering Change Requests
(ECR) with ALM work items
Integration from Sodius between
PTC Windchill and IBM CLM:
Integration from Siemens between
Siemens Teamcenter and Polarion
https://docs.plm.automation.siemens.com/data_services/reso
urces/polarion/17.3/help/common/en_US/graphics/fileLibrary/
17_3_pdfs/Teamcenter_Polarion_Integration_Installation.pdf
OSLC API for Contact Elements
● Reference implementation in Python
released as open-source soon
● OSLC API for Contact Elements can be
used to connect PLM/ALM artifacts and
more
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15. What does it mean to connect data?
Example: Requirement identifier <- link type -> Simulation parameter identifier
Connection is between IDENTIFIERS of data
Analogy: phone call between 2 persons identified by their phone number
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Artifact ID1 Artifact ID2
Link Type ID1

16. What does it mean to connect data?
Example: Requirement identifier <- link type -> Simulation parameter identifier
Connection is between IDENTIFIERS of data
Analogy: phone call between 2 persons identified by their phone number
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Artifact ID1 Artifact ID2
Link Type ID1
Artifact ID2Link Type ID2
Link Type
ID3

17. Application Programming Interface (API)
An API can specify the
interface to a database,
software, dataset
De facto API standard
now: REST APIs
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Facebook Google Twitter
API1 API2 API3

18. REST API Example 1
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Facebook
GET
"https://graph.facebook.com/{your-user-id}
?fields=id,name "
{
"name": "Your Name",
"id": "your-user-id"
}

19. REST API Example 2
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Dropbox
POST
https://api.dropboxapi.com/2/users/get_account
--data "{"account_id": "dbid:AAH4f99..."}"
{
"account_id": "dbid:AAH4f99..."
}

20. Data Integration Requires Data Transformations
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Data
Repository 1
Data
Repository 2
Data
Repository 3
Data Integration Data Transformation
Data Sources
ID = 1223465
ID = http://xyz/123
ID = e0d123e5f31
Different local ID formats
Data unification using
data transformations

21. Other Data Aspects Requiring Data Transformations
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Data
Repository 1
Data
Repository 2
Data
Repository 3
Data Integration Data Transformation
Data Sources
Universal Data
Aspects
Identifier
Version
Change events
Data model
Projects
Data unification using
data transformations

22. Reality
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Data
Repository 1
Data
Repository 2
Data
Repository 3
Data Integration Data Transformation
Data Sources
Limited
number of
integrations
TOO MUCH EFFORT -> Limited
number of data transformations
Limited data
integration

23. Connecting data is currently impossible!
Full
connectivity
CAD Software Simulation
API1 API2 API3
Different APIs everywhere!
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24. Different API = vendor lock-in
Your Data Your Software
Application Vendor
Proprietary APIs
and Data Formats
$ $
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25. Once Upon a Time - Before the Web
Different protocols to access documents on the internet (Gopher, WAIS, etc...)
No connected documents (hierarchical document structure, no hyperlinks)
Not many persons used the internet
Hypertext existed since 1965, 25 years before the invention of the Web
Lack of standards for Hypertext hindered adoption of Hypertext - no compatibility
between different Hypertext systems
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26. Lessons learned from the Web
Seeds for innovation: Open standards + open-source
Web not owned by a software vendor
Any document can connect to any other document
Improved knowledge sharing and collaboration
OSLC driven by similar values than World Wide Web
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27. Reusable Web Standards
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Data
Repository
HTTP
?
URL http://../req2

28. Machine-readable format for links
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Needed: format to describe graphs
Different standard data models to describe graphs
Currently most used for data on the Web: Resource Description Framework (RDF)
Artifact ID1 Artifact ID2
Link Type ID1
Artifact ID2Link Type ID2
Link Type
ID3

29. RDF Example: Schema.org
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Interoperability between search engines and Web
content creators through schema.org

30. Standard APIs for Connecting Data
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Requirements Test cases Simulation
API1 API2 API3
Global_Id1
Global_Id2
Global_Id3
Global_Id4
Global_Id5
Global_Id6
Standard
API
Standard
API
Standard
APIHTTP
URLRDF

31. Open Services for Lifecycle Collaboration (OSLC)
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From http://docs.oasis-open.org/oslc-core/oslc-core/v3.0/oslc-core-v3.0-part1-overview.html
Web Standards
OSLC Standards

32. OSLC Adoption
By vendors mostly in ALM: IBM Jazz/CLM,
Mentor Graphics, Sodius
Over 50 OSLC APIs developed for different
applications
By universities and consultants for MBSE,
simulation: https://github.com/ld4mbse +
https://github.com/oslc
General Motors in their MBSE
efforts and vision
https://koneksys.com/blog/present
ation-of-oslc-at-purdue-plm-meeti
ng-2018/
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33. Applications decoupled from data
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Requirements Test cases Simulation
API1 API2 API3
Visualization
Search/Query
Reporting
Modular
architecture
Combining
Applications
and data
through
plug-n-play
Standard
API
Standard
API
Standard
API
Machine
Learning
Link
Management
Modeling

34. Private/public
Data Web
Distributed
Data Silos
Mashup
Application
Example
Google-like
Search
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Data
Repository 1
Data
Repository 2
Data
Repository 3
RDF Link Link RDF

35. Discipline-specific vs. Universal Data Management
Currently, we have discipline-specific data management: Simulation Data
Management (SDM), Product Lifecycle Management (PLM), Application Lifecycle
Management (ALM) etc., and NO Data connectivity
Universal Data Management is needed
● Viewing data as a universal asset
● Viewing data as equal
● Viewing data with its context
● Viewing data as a whole
● Using open standards for creating standard APIs
● Creating applications on top of standard APIs
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36. What now?
Request better APIs from vendors
Request OSLC APIs from vendors
Adopt OSLC in your new integration
projects
Perform POC projects with OSLC
Help build the OSLC community
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37. Thanks and get in touch!
axel.reichwein@koneksys.com