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Open Services for Lifecycle Collaboration -
Extending REST APIs to Connect Data
18th International Semantic Web Conference
Auckland, New Zealand
Axel Reichwein and Fernando Lopez, Koneksys
October 29, 2019
1
About Me - Fernando Lopez
2
Machine
learning
Graph
Mining
Graph Theory
Link Prediction
Natural Language
Processing
Koneksys
Koneksys Consulting Services
Connecting data using standards
● Custom software development
for organizations and vendors
● Research projects
3
Status Quo in Engineering 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
● 54% of our decisions are made with
inconsistent, or incomplete, or
inadequate information
Connected Data London, 2016
Quote from https://www.youtube.com/watch?v=QEBVoultYJg
4
URLs of reused images related to architecture, 3D, control
Challenge: Crosscutting Concerns Across Disciplines
5
Requirements
Engineering
Design Manufacturing Operation
Traceability
Impact analysis
Configuration
management
Change
Management
URLs of reused images related to requirements, design, manufacturing, operation
Example Relationships in Engineering
6
Requirement
Test
Case
Simulation Model
3D Model
FEDERAL AVIATION ADMINISTRATION (FAA)
Title 14, Chapter I, Subchapter C
§25.121
...(a) Takeoff; landing gear
extended... the steady
gradient of climb...not less
than 0.5 percent...for four-
engine airplanes
<<requirement>>
Climb: One-engine-inoperative
Perform simulation under
conditions...
<<testcase>>
Climb: One-engine-inoperative
Requirement
Test
Case
3D Model
Simulation Model
Control System Model
Graph for Describing All Relationships
7
Requirement
Test
Case
Simulation
Model
3D Model
Graph nodes represent any data
element, such as a project, a
model, or a parameter inside a
model, or a requirement etc.
Control System Model
Graph edges
represent
relationships, having
a type and a direction
depends_on
depends_on
Engineering Graph for Global-Level System Analysis
8
I want to understand
the impact of
changing this
requirement
I want to understand
how this requirement
is implemented
I want to go through
what-if scenarios to
find the best
architecture
I want to understand
the origin of a product
failure
System EngineerRequirement Engineer
Quality EngineerMechanical Engineer
Engineering
Graph
Navigable Engineering Graph
9
Click on related
test case and
see test case
representation
Click on
related model
and see model
representation
Configuration-Managed Sub-Graphs and Eng. Graphs
10
Requirement Project X
Version 1.3
Test Case Project Y
Version 3.0
Control System Model
Version h56278d33
Each engineering
data element belongs
to a sub-graph which
is under version-
management
Composable Engineering Graphs
11
Engineering Graph of
Subsystem 1 version 4
• Requirement Project A
• Test Case Project B
• Simulation Model C
Engineering Graph of
Subsystem 2 version 7
• Requirement Project D
• Test Case Project E
• Simulation Model F
Links between
data elements of
Subsystem 1 and
Subsystem 2
Engineering Graph of
System version 1
• Requirement Project A
• Test Case Project B
• Simulation Model C
• Requirement Project D
• Test Case Project E
• Simulation Model F
• Links between data
elements of Subsystem 1
and Subsystem 2
Steps for Building Engineering Graph
12
Data sources
Data in a common graph
format conforming to
common vocabularies for
generic data aspects
Engineering Graph
Data
transformation
Ingesting
graphs into one
single graph
and adding links
API 1
Data
Source 1
API 2
Data
Source 2
Challenge: 500+ Different Data Transformations!
13
Data sources
Data in a common graph
format conforming to
common vocabularies for
generic data aspects
Data transformation 1
API 1
Data
Source 1
API 2
Data
Source 2
500+ Different Data Sources
500+ Different APIs
Many different vocabularies
for the same concepts
Implementing 500+ different data
transformations is a challenge
Data transformation 2
Status Quo - No Complete Engineering Graph
Many data silos
Many links captured in non-
machine readable
documents
Problems are discovered
late in the design process,
and the later they are
discovered, the more
expensive it is to fix them
14
Standard API to Reduce Data Transformation Effort
15
Engineering Graph
Ingesting
graphs into one
single graph
and adding links
Data
Source 1
Data
Source 2
No need for data
transformations
Data and API heterogeneity problem
reduced to API-compliance problem
Standard API
Standard API
Data in a common graph
format conforming to
common vocabularies for
generic data aspects
Standardized Generic Data Aspects
16
Data
Source 1
Data
Source 2
Standard API
Standard API
Data in a common graph
format conforming to
common vocabularies for
generic data concepts
Generic Data Concepts
Standardized by OSLC
• Data identifier
• Configuration/Version-
Management
• Change events
• Data model/constraints
• Data containers/projects
Standardized Discipline-specific Concepts
● Some discipline-specific RDF vocabularies
are defined by OSLC to support data
interoperability
● Discipline-specific RDF vocabularies
defined by OSLC are not the main
contribution of OSLC!
● General Problems of discipline-specific
standards/vocabularies: likely to change
over time, and lack of consensus
17
Discipline-specific Concepts
Standardized by OSLC
• Requirements
Management
• Quality Management
• Asset Management
• Automation
• Architecture Management
• Performance Monitoring
OSLC API compared to traditional REST API
18
API/Data Concept Traditional REST API OSLC API
Protocol HTTP HTTP
Resource identifier
format
Often internal ID like an
integer number
HTTP URL, as in Linked Data (always
dereferenceable and always unique)
Resource
representation format
JSON (key-value pairs) RDF (JSON-LD, RDF/XML, Turtle,
etc.)
Documentation of API
endpoints
OpenAPI “RDF-version” of OpenAPI defined by
OSLC Core Discovery Spec
Resource schema
format
OpenAPI or JSON
Schema or not available
OSLC Shapes or SHACL
OSLC Enables New Mashup Applications
19
Data
Source 1
Data
Source 2
Standard API
Standard API
New Mashup
Applications:
• Full-text search
• Visualization
• Reporting
• Design Review
• Link prediction
• And many more
Decoupling between data and
application by using standard API
OSLC Adoption – APIs and Mashup Applications
20
Data
Source 1
Data
Source 2
Standard API
Standard API
50+ OSLC APIs for
different data sources
Existing OSLC-based Mashup Applications
Vendor Application
IBM • Lifecycle Query Engine
• Global Configuration
Management
• Jazz Reporting Service
Mentor
Graphics
Context
Sodius SECollab
MID Smartfacts
PTC Integrity Modeler
OSLC Adoption - Pros & Cons
Pros
Started in 2008. Used by major
aerospace and automotive companies
Similar to the recent Solid initiative
led by Tim Berners-Lee
Many open-source OSLC solutions at
Eclipse Lyo
Cons
Surprisingly not known to the
Semantic Web community
No support from software vendors
who fear losing vendor lock-in
OSLC considered complex. Better
documentation and demos needed
21
To Learn More
http://oslcfest.org/
22
https://open-services.net/
Conclusion
Engineers designing complex systems need
traceability, thus engineering graphs
Building (engineering) graphs at scale
requires an API standard
OSLC is an API standard combining concepts
from REST and Linked Data
We need to build graphs in other domains like
healthcare
23
Data
Source Standard API
Thanks and get in touch!
fernando.lopez@koneksys.com

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Open Services for Lifecycle Collaboration (OSLC) - Extending REST APIs to Connect Data

  • 1. Open Services for Lifecycle Collaboration - Extending REST APIs to Connect Data 18th International Semantic Web Conference Auckland, New Zealand Axel Reichwein and Fernando Lopez, Koneksys October 29, 2019 1
  • 2. About Me - Fernando Lopez 2 Machine learning Graph Mining Graph Theory Link Prediction Natural Language Processing Koneksys
  • 3. Koneksys Consulting Services Connecting data using standards ● Custom software development for organizations and vendors ● Research projects 3
  • 4. Status Quo in Engineering 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 ● 54% of our decisions are made with inconsistent, or incomplete, or inadequate information Connected Data London, 2016 Quote from https://www.youtube.com/watch?v=QEBVoultYJg 4 URLs of reused images related to architecture, 3D, control
  • 5. Challenge: Crosscutting Concerns Across Disciplines 5 Requirements Engineering Design Manufacturing Operation Traceability Impact analysis Configuration management Change Management URLs of reused images related to requirements, design, manufacturing, operation
  • 6. Example Relationships in Engineering 6 Requirement Test Case Simulation Model 3D Model FEDERAL AVIATION ADMINISTRATION (FAA) Title 14, Chapter I, Subchapter C §25.121 ...(a) Takeoff; landing gear extended... the steady gradient of climb...not less than 0.5 percent...for four- engine airplanes <<requirement>> Climb: One-engine-inoperative Perform simulation under conditions... <<testcase>> Climb: One-engine-inoperative Requirement Test Case 3D Model Simulation Model Control System Model
  • 7. Graph for Describing All Relationships 7 Requirement Test Case Simulation Model 3D Model Graph nodes represent any data element, such as a project, a model, or a parameter inside a model, or a requirement etc. Control System Model Graph edges represent relationships, having a type and a direction depends_on depends_on
  • 8. Engineering Graph for Global-Level System Analysis 8 I want to understand the impact of changing this requirement I want to understand how this requirement is implemented I want to go through what-if scenarios to find the best architecture I want to understand the origin of a product failure System EngineerRequirement Engineer Quality EngineerMechanical Engineer Engineering Graph
  • 9. Navigable Engineering Graph 9 Click on related test case and see test case representation Click on related model and see model representation
  • 10. Configuration-Managed Sub-Graphs and Eng. Graphs 10 Requirement Project X Version 1.3 Test Case Project Y Version 3.0 Control System Model Version h56278d33 Each engineering data element belongs to a sub-graph which is under version- management
  • 11. Composable Engineering Graphs 11 Engineering Graph of Subsystem 1 version 4 • Requirement Project A • Test Case Project B • Simulation Model C Engineering Graph of Subsystem 2 version 7 • Requirement Project D • Test Case Project E • Simulation Model F Links between data elements of Subsystem 1 and Subsystem 2 Engineering Graph of System version 1 • Requirement Project A • Test Case Project B • Simulation Model C • Requirement Project D • Test Case Project E • Simulation Model F • Links between data elements of Subsystem 1 and Subsystem 2
  • 12. Steps for Building Engineering Graph 12 Data sources Data in a common graph format conforming to common vocabularies for generic data aspects Engineering Graph Data transformation Ingesting graphs into one single graph and adding links API 1 Data Source 1 API 2 Data Source 2
  • 13. Challenge: 500+ Different Data Transformations! 13 Data sources Data in a common graph format conforming to common vocabularies for generic data aspects Data transformation 1 API 1 Data Source 1 API 2 Data Source 2 500+ Different Data Sources 500+ Different APIs Many different vocabularies for the same concepts Implementing 500+ different data transformations is a challenge Data transformation 2
  • 14. Status Quo - No Complete Engineering Graph Many data silos Many links captured in non- machine readable documents Problems are discovered late in the design process, and the later they are discovered, the more expensive it is to fix them 14
  • 15. Standard API to Reduce Data Transformation Effort 15 Engineering Graph Ingesting graphs into one single graph and adding links Data Source 1 Data Source 2 No need for data transformations Data and API heterogeneity problem reduced to API-compliance problem Standard API Standard API Data in a common graph format conforming to common vocabularies for generic data aspects
  • 16. Standardized Generic Data Aspects 16 Data Source 1 Data Source 2 Standard API Standard API Data in a common graph format conforming to common vocabularies for generic data concepts Generic Data Concepts Standardized by OSLC • Data identifier • Configuration/Version- Management • Change events • Data model/constraints • Data containers/projects
  • 17. Standardized Discipline-specific Concepts ● Some discipline-specific RDF vocabularies are defined by OSLC to support data interoperability ● Discipline-specific RDF vocabularies defined by OSLC are not the main contribution of OSLC! ● General Problems of discipline-specific standards/vocabularies: likely to change over time, and lack of consensus 17 Discipline-specific Concepts Standardized by OSLC • Requirements Management • Quality Management • Asset Management • Automation • Architecture Management • Performance Monitoring
  • 18. OSLC API compared to traditional REST API 18 API/Data Concept Traditional REST API OSLC API Protocol HTTP HTTP Resource identifier format Often internal ID like an integer number HTTP URL, as in Linked Data (always dereferenceable and always unique) Resource representation format JSON (key-value pairs) RDF (JSON-LD, RDF/XML, Turtle, etc.) Documentation of API endpoints OpenAPI “RDF-version” of OpenAPI defined by OSLC Core Discovery Spec Resource schema format OpenAPI or JSON Schema or not available OSLC Shapes or SHACL
  • 19. OSLC Enables New Mashup Applications 19 Data Source 1 Data Source 2 Standard API Standard API New Mashup Applications: • Full-text search • Visualization • Reporting • Design Review • Link prediction • And many more Decoupling between data and application by using standard API
  • 20. OSLC Adoption – APIs and Mashup Applications 20 Data Source 1 Data Source 2 Standard API Standard API 50+ OSLC APIs for different data sources Existing OSLC-based Mashup Applications Vendor Application IBM • Lifecycle Query Engine • Global Configuration Management • Jazz Reporting Service Mentor Graphics Context Sodius SECollab MID Smartfacts PTC Integrity Modeler
  • 21. OSLC Adoption - Pros & Cons Pros Started in 2008. Used by major aerospace and automotive companies Similar to the recent Solid initiative led by Tim Berners-Lee Many open-source OSLC solutions at Eclipse Lyo Cons Surprisingly not known to the Semantic Web community No support from software vendors who fear losing vendor lock-in OSLC considered complex. Better documentation and demos needed 21
  • 23. Conclusion Engineers designing complex systems need traceability, thus engineering graphs Building (engineering) graphs at scale requires an API standard OSLC is an API standard combining concepts from REST and Linked Data We need to build graphs in other domains like healthcare 23 Data Source Standard API
  • 24. Thanks and get in touch! fernando.lopez@koneksys.com