Open Services for Lifecycle Collaboration (OSLC) - Extending REST APIs to Con...Axel Reichwein
Presentation on Open Services for Lifecycle Collaboration (OSLC) at the International Semantic Web Conference (ISWC) 2019 in Auckland, New Zealand.
Engineers need graphs for traceability. Problem: it is currently not possible to build engineering graphs at scale due to data and API heterogeneity. This problem can be solved by standardizing APIs of data sources. OSLC defines a standard API by combining concepts of REST and Linked Data. OSLC has been adopted by vendors of engineering software but more adoption is needed to increase the network effect.
Introduction to Open Services for Lifecycle Collaboration (OSLC)Axel Reichwein
Quick Introduction on OSLC APIs For Digital Engineering Information Exchange (DEIX) Community explaining fundamental ideas behind OSLC including:
- Distributed Link Creation Strategy from within ANY application
- Link creation decoupled from link persistence/analysis/visualization
- Embeddable search dialogs to support user-friendly link target discovery in any application
- Standardized API discovery resources (Hypermedia API)
Open Services for Lifecycle Collaboration (OSLC) - Extending REST APIs to Con...Axel Reichwein
Presentation on Open Services for Lifecycle Collaboration (OSLC) at the International Semantic Web Conference (ISWC) 2019 in Auckland, New Zealand.
Engineers need graphs for traceability. Problem: it is currently not possible to build engineering graphs at scale due to data and API heterogeneity. This problem can be solved by standardizing APIs of data sources. OSLC defines a standard API by combining concepts of REST and Linked Data. OSLC has been adopted by vendors of engineering software but more adoption is needed to increase the network effect.
Introduction to Open Services for Lifecycle Collaboration (OSLC)Axel Reichwein
Quick Introduction on OSLC APIs For Digital Engineering Information Exchange (DEIX) Community explaining fundamental ideas behind OSLC including:
- Distributed Link Creation Strategy from within ANY application
- Link creation decoupled from link persistence/analysis/visualization
- Embeddable search dialogs to support user-friendly link target discovery in any application
- Standardized API discovery resources (Hypermedia API)
The Web is until now mostly considered to be a Web of documents, more specifically a Web of HTML pages. However, the inventor of the Web Tim Berners Lee considers the Web not to have reached its fullest potential. The Data Web and Linked Data will enable more precise search services transforming the Web into a smarter and richer Web. Google for example uses Linked Data concepts to realize its own knowledge graph to process voice commands and voice queries for users. Linked Data concepts are not limited to the public Web. They can also be used to capture private knowledge in private company Webs making them potentially applicable as the backbone for future PLM solutions.
A COMPARATIVE STUDY BETWEEN GRAPH-QL& RESTFUL SERVICES IN API MANAGEMENT OF S...ijwscjournal
A stateless architecture design is a web architecture design that typically does not persist data in any database and such applications also does not require any kind of backup storage. Data that flows through a stateless service is data in transition and such data is never stored in any data store. The processing
requests that arrive to such architecture does not rely on information gathered or persisted from any previous session. API (Application programming interface) which consists of subroutines, definitions & procedures that can access data on the applications are the communication points between applications and management of API endpoints using stateless architecture is less complex as there is no server side retention of the client session and each client sends requisite information in each request to the server. GraphQL and RESTful services are means of designing such API architecture. This paper discusses and explains in detail both GraphQL and REST API architecture design and management methods and does an
analysis of the potential benefits of GraphQL over REST in Stateless architectural API designs.
Towards a Resource Slice Interoperability Hub for IoTHong-Linh Truong
Interoperability for IoT is a challenging problem
because it requires us to tackle (i) cross-system interoperability
issues at the IoT platform sides as well as relevant network
functions and clouds in the edge systems and data centers
and (ii) cross-layer interoperability, e.g., w.r.t. data formats,
communication protocols, data delivery mechanisms, and perfor-
mance. However, existing solutions are quite static w.r.t software
deployment and provisioning for interoperability. Many middle-
ware, services and platforms have been built and deployed as
interoperability bridges but they are not dynamically provisioned
and reconfigured for interoperability at runtime. Furthermore,
they are often not considered together with other services as a
whole in application-specific contexts. In this paper, we focus
on dynamic aspects by introducing the concept of Resource
Slice Interoperability Hub (rsiHub). Our approach leverages
existing software artifacts and services for interoperability to
create and provision dynamic resource slices, including IoT,
network functions and clouds, for addressing application-specific
interoperability requirements. We will present our key concepts,
architectures and examples toward the realization of rsiHub.
Learn how to build advanced GraphQL queries, how to work with filters and patches and how to embed GraphQL in languages like Python and Java. These slides are the second set in our webinar series on GraphQL.
This Slid shows the GraphQL Fundamentals, cover below points.
* what: what is GraphQL
* who: Who builds GraphQL
* why: Why we need GraphQL
* how: How to use GraphQL
IT also cover
* Application Programming Interface (API History Image )
* Principles of REST API Design
* REST Flow / GraphQL Flow diagram
* Rest Traditional Data fetching.
* REST vs GraphQL
* Browser Tools
* GraphQL Mutation
* GraphQL Variables
* GraphQL Fragments
* Pain Points of GraphQL
In this presentation, Suraj Kumar Paul of Valuebound has walked us through GraphQL. Founded by Facebook in 2012, GraphQL is a data query language that provides an alternative to REST and web service architectures.
Here he has discussed core ideas of GraphQL, limitations of RESTful APIs, operations, arguments, fragmentation, variables, mutations etc.
----------------------------------------------------------
Get Socialistic
Our website: http://valuebound.com/
LinkedIn: http://bit.ly/2eKgdux
Facebook: https://www.facebook.com/valuebound/
SLAS 2017 - "Multiple Research Platforms: One Single Data Sharing Portal"CSols, Inc.
Electronic Laboratory Notebook (ELN) Presentation by Farid Said of CSols, Inc. (the Premier Laboratory Informatics Experts).
For more information and any questions visit: http://www.csolsinc.com
GraphQL as an alternative approach to REST (as presented at Java2Days/CodeMon...luisw19
Originally designed by Facebook to allow its mobile clients to define exactly what data should be send back by an API and therefore avoid unnecessary roundtrips and data usage, GraphQL is a JSON based query language for Web APIs. Since it was open sourced by Facebook in 2015, it has undergone very rapid adoption and many companies have already switch to the GraphQL way of building APIs – see http://GraphQL.org/users.
However, with some many hundreds of thousands of REST APIs publicly available today (and many thousands others available internally), what are the implications of moving to GraphQL? Is it really worth the effort of replacing REST APIs specially if they’re successful and performing well in production? What are the pros/cons of using GraphQL? What tools / languages can be used for GraphQL? What about API Gateways? What about API design?
With a combination of rich content and hands-on demonstrations, attend this session for a point of view on how address these and many other questions, and most importantly get a better understanding and when/where/why/if GraphQL applies for your organisation or specific use case.
Using DITAworks for Eclipse Help publishingwild_wild_leha
This presentation shows how DITAworks adds value to process of single source documentation management. It also focuses on specific support of Eclipse Help features as one of the target publishing formats.
Tutorial Workgroup - Model versioning and collaborationPascalDesmarets1
Hackolade Studio has native integration with Git repositories to provide state-of-the-art collaboration, versioning, branching, conflict resolution, peer review workflows, change tracking and traceability. Mostly, it allows to co-locate data models and schemas with application code, and further integrate with DevOps CI/CD pipelines as part of our vision for Metadata-as-Code.
Co-located application code and data models provide the single source-of-truth for business and technical stakeholders.
Watch full webinar here: https://bit.ly/3mdj9i7
You will often hear that "data is the new gold"? In this context, data management is one of the areas that has received more attention from the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
In this webinar, we will discuss the technology trends that will drive the enterprise data strategies in the years to come. Don't miss it if you want to keep yourself informed about how to convert your data to strategic assets in order to complete the data-driven transformation in your company.
Watch this on-demand webinar as we cover:
- The most interesting trends in data management
- How to build a data fabric architecture?
- How to manage your data integration strategy in the new hybrid world
- Our predictions on how those trends will change the data management world
- How can companies monetize the data through data-as-a-service infrastructure?
- What is the role of voice computing in future data analytic
The Web is until now mostly considered to be a Web of documents, more specifically a Web of HTML pages. However, the inventor of the Web Tim Berners Lee considers the Web not to have reached its fullest potential. The Data Web and Linked Data will enable more precise search services transforming the Web into a smarter and richer Web. Google for example uses Linked Data concepts to realize its own knowledge graph to process voice commands and voice queries for users. Linked Data concepts are not limited to the public Web. They can also be used to capture private knowledge in private company Webs making them potentially applicable as the backbone for future PLM solutions.
A COMPARATIVE STUDY BETWEEN GRAPH-QL& RESTFUL SERVICES IN API MANAGEMENT OF S...ijwscjournal
A stateless architecture design is a web architecture design that typically does not persist data in any database and such applications also does not require any kind of backup storage. Data that flows through a stateless service is data in transition and such data is never stored in any data store. The processing
requests that arrive to such architecture does not rely on information gathered or persisted from any previous session. API (Application programming interface) which consists of subroutines, definitions & procedures that can access data on the applications are the communication points between applications and management of API endpoints using stateless architecture is less complex as there is no server side retention of the client session and each client sends requisite information in each request to the server. GraphQL and RESTful services are means of designing such API architecture. This paper discusses and explains in detail both GraphQL and REST API architecture design and management methods and does an
analysis of the potential benefits of GraphQL over REST in Stateless architectural API designs.
Towards a Resource Slice Interoperability Hub for IoTHong-Linh Truong
Interoperability for IoT is a challenging problem
because it requires us to tackle (i) cross-system interoperability
issues at the IoT platform sides as well as relevant network
functions and clouds in the edge systems and data centers
and (ii) cross-layer interoperability, e.g., w.r.t. data formats,
communication protocols, data delivery mechanisms, and perfor-
mance. However, existing solutions are quite static w.r.t software
deployment and provisioning for interoperability. Many middle-
ware, services and platforms have been built and deployed as
interoperability bridges but they are not dynamically provisioned
and reconfigured for interoperability at runtime. Furthermore,
they are often not considered together with other services as a
whole in application-specific contexts. In this paper, we focus
on dynamic aspects by introducing the concept of Resource
Slice Interoperability Hub (rsiHub). Our approach leverages
existing software artifacts and services for interoperability to
create and provision dynamic resource slices, including IoT,
network functions and clouds, for addressing application-specific
interoperability requirements. We will present our key concepts,
architectures and examples toward the realization of rsiHub.
Learn how to build advanced GraphQL queries, how to work with filters and patches and how to embed GraphQL in languages like Python and Java. These slides are the second set in our webinar series on GraphQL.
This Slid shows the GraphQL Fundamentals, cover below points.
* what: what is GraphQL
* who: Who builds GraphQL
* why: Why we need GraphQL
* how: How to use GraphQL
IT also cover
* Application Programming Interface (API History Image )
* Principles of REST API Design
* REST Flow / GraphQL Flow diagram
* Rest Traditional Data fetching.
* REST vs GraphQL
* Browser Tools
* GraphQL Mutation
* GraphQL Variables
* GraphQL Fragments
* Pain Points of GraphQL
In this presentation, Suraj Kumar Paul of Valuebound has walked us through GraphQL. Founded by Facebook in 2012, GraphQL is a data query language that provides an alternative to REST and web service architectures.
Here he has discussed core ideas of GraphQL, limitations of RESTful APIs, operations, arguments, fragmentation, variables, mutations etc.
----------------------------------------------------------
Get Socialistic
Our website: http://valuebound.com/
LinkedIn: http://bit.ly/2eKgdux
Facebook: https://www.facebook.com/valuebound/
SLAS 2017 - "Multiple Research Platforms: One Single Data Sharing Portal"CSols, Inc.
Electronic Laboratory Notebook (ELN) Presentation by Farid Said of CSols, Inc. (the Premier Laboratory Informatics Experts).
For more information and any questions visit: http://www.csolsinc.com
GraphQL as an alternative approach to REST (as presented at Java2Days/CodeMon...luisw19
Originally designed by Facebook to allow its mobile clients to define exactly what data should be send back by an API and therefore avoid unnecessary roundtrips and data usage, GraphQL is a JSON based query language for Web APIs. Since it was open sourced by Facebook in 2015, it has undergone very rapid adoption and many companies have already switch to the GraphQL way of building APIs – see http://GraphQL.org/users.
However, with some many hundreds of thousands of REST APIs publicly available today (and many thousands others available internally), what are the implications of moving to GraphQL? Is it really worth the effort of replacing REST APIs specially if they’re successful and performing well in production? What are the pros/cons of using GraphQL? What tools / languages can be used for GraphQL? What about API Gateways? What about API design?
With a combination of rich content and hands-on demonstrations, attend this session for a point of view on how address these and many other questions, and most importantly get a better understanding and when/where/why/if GraphQL applies for your organisation or specific use case.
Using DITAworks for Eclipse Help publishingwild_wild_leha
This presentation shows how DITAworks adds value to process of single source documentation management. It also focuses on specific support of Eclipse Help features as one of the target publishing formats.
Tutorial Workgroup - Model versioning and collaborationPascalDesmarets1
Hackolade Studio has native integration with Git repositories to provide state-of-the-art collaboration, versioning, branching, conflict resolution, peer review workflows, change tracking and traceability. Mostly, it allows to co-locate data models and schemas with application code, and further integrate with DevOps CI/CD pipelines as part of our vision for Metadata-as-Code.
Co-located application code and data models provide the single source-of-truth for business and technical stakeholders.
Watch full webinar here: https://bit.ly/3mdj9i7
You will often hear that "data is the new gold"? In this context, data management is one of the areas that has received more attention from the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
In this webinar, we will discuss the technology trends that will drive the enterprise data strategies in the years to come. Don't miss it if you want to keep yourself informed about how to convert your data to strategic assets in order to complete the data-driven transformation in your company.
Watch this on-demand webinar as we cover:
- The most interesting trends in data management
- How to build a data fabric architecture?
- How to manage your data integration strategy in the new hybrid world
- Our predictions on how those trends will change the data management world
- How can companies monetize the data through data-as-a-service infrastructure?
- What is the role of voice computing in future data analytic
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Install the Hackolade Studio CLI on a server, and trigger it to run concurrent multi-threaded sessions in a Docker container, as part of that environment. As part of your CI/CD pipeline, you can trigger data modeling automations and have it perform things like creation of artifacts, forward- and reverse-engineering, model comparisons, documentation generation, ...
http://www.opitz-consulting.com/go/3-8-11
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OPITZ CONSULTING war als strategischer Partner und als einer der führenden Protagonisten im Cloud Computing bei der Veranstaltung präsent und wirkte als Platinum-Sponsor aktiv an der inhaltlichen Gestaltung des Tracks “Application Developers” mit.
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Cloud Services” stellte Torsten Winterberg, Oracle ACE Director und SOA- und BPM-Experte unserer IT-Beratung, die unterschiedlichen Ansätze zur Entwicklung von Lösungen in der Cloud und für die Cloud vor. Dabei ging er konkret auf die Entwicklungsumgebungen APEX und ADF ein, um das Thema Integration und Architektur in der Cloud intensiv zu beleuchten.
--
Über uns:
Als führender Projektspezialist für ganzheitliche IT-Lösungen tragen wir zur Wertsteigerung der Organisationen unserer Kunden bei und bringen IT und Business in Einklang. Mit OPITZ CONSULTING als zuverlässigem Partner können sich unsere Kunden auf ihr Kerngeschäft konzentrieren und ihre Wettbewerbsvorteile nachhaltig absichern und ausbauen.
Über unsere IT-Beratung: http://www.opitz-consulting.com/go/3-8-10
Unser Leistungsangebot: http://www.opitz-consulting.com/go/3-8-874
Karriere bei OPITZ CONSULTING: http://www.opitz-consulting.com/go/3-8-5
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Gartner a récemment qualifié la Data Virtualisation comme étant une pièce maitresse des architectures d’intégration de données.
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Watch here: https://bit.ly/3i2iJbu
You will often hear that "data is the new gold". In this context, data management is one of the areas that has received more attention by the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
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Achieving the digital thread through PLM and ALM integration using oslc
1. Achieving the digital thread through PLM
and ALM integration using OSLC
Purdue PLM Meeting Spring 2018
Axel Reichwein
March 29, 2018
Koneksys
2. Axel Reichwein
● Developer of multiple data integration
solutions based on Open Services for
Lifecycle Collaboration (OSLC)
● Background in aerospace engineering
● Since PhD, focus on data integration
● Since Koneksys, focus on OSLC
● Previously involved in standardization
efforts related to SysML (Systems
Modeling Language)
● Presented OSLC at multiple conferences:
INCOSE, OMG, SAE International
Automotive, North American Modelica
Users Group, IBM InterConnect, IBM
Innovate, NoMagic World Conference,
CIMdata Systems Engineering Workshop
2
3. Status Quo of Collaboration
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”
https://www.youtube.com/watch?v=QEBVoultYJg
3
5. Distributed Engineering Information
One technical system
described from different
perspectives
One technical system, but a lot
of distributed information
Distributed information is
challenging for collaboration
5
Software
Costs
SpreadsheetsReports
Test casesRequirements 3D Geometry
Behavior
Technical
System
6. Overlaps and Relationships in Engineering Information
Overlaps due to data duplication
(e.g. same parameter used in
different models or reports)
Logical relationships such as a
requirement verified by a test
case
The more complex a system is, the
more relationships exist between
engineering information
6
7. Problem: Rollover Risk of SUVs
Higher center of gravity -> higher risk of rollover
More than a third of all fatal crashes in the US are rollovers!
http://www.cars.com/go/crp/buyingGuides/Story.jsp?section=SUV&story=suvSafe2012&subject=stories&referer=&year=New
7
13. Example Digital Thread of PLM vendor
13
Requirements
Engineering
Design Manufacturing Operation Problems
● Limited
integration of
specific disciplines
and software
applications
● No mix-n-match as
needed by your
organization (No
ad-hoc integration)
● Custom
integration
development is
expensive
● Locked in by
vendor
Parts
CAD
docu-
ments
Require-
ments
Archi-
tecture
Process
Plan
MBOM Diagnosis
Software
Operatio-
nal Data
14. Crosscutting Concerns Across Disciplines
14
Requirements
Engineering
Design Manufacturing Operation
Traceability
Configuration
management
Trade-off studies
Problem
resolution
15. Collaboration Challenges in Designing Systems
15
Increasing
system
complexity
Increasing
number of
meetings
Increasing
costs
Increasing
number of
partners
Increasing
number of
versions of data
Increasing
frustration
How can I assess
the impact of a
change?
How can I
establish
traceability
How do I know
what is related to
what?
How can I manage
changes/updates?
16. Data Integration Benefits
16
Understanding
the context of
information
Performing
consolidated
reporting
Performing
data analysis
Understanding
the ripple effects
of changes
Understanding
the origin of
product failures
Performing
better decisions
17. Key Data Integration Concepts and Standards
1. Standard machine-readable data format = RDF
2. Standard to identify data = URL
3. Standard to access data = HTTPHTTP
RDF
URL
● No license costs
● No vendor lock-in
● Mature and widely adopted
infrastructure
● Abundance of Web
specialists/developers
17
18. Hypertext + Internet = Web
18
Hypertext System 1 Hypertext System 2
Problem: No Compatibility between
hypertext systems + different protocols to
access and connect documents on the
internet (Gopher, WAIS, etc...)
BEFORE THE WEB
One global hypertext system = Web
One protocol to access and connect
documents
WITH THE WEB
19. Extending Web of documents to a Web of Data
Requirements PLM ERPFacebook Server Wikipedia Server Gmail Server
Note: a lot of
information
accessible through
the Web is private!
Documents spread across
multiple machines
Data spread across
multiple databases
Web of Documents Web of Data
19
20. URLs = Common Global Information Identifiers
Data Repository 1 Data Repository 2 Data Repository 3
wikipedia.org
facebook.com
https://private.myorg.com/req123
https://private.supplier.com/part123
Data Repository 1 Data Repository 2 Data Repository 3
myblog.com
Web of Documents Web of Data
OSLC
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21. HTTP = Common Protocol to Access Information
OSLC specifies
how to perform
CRUD
operations on
data using HTTP
Web of Documents Web of Data
OSLC
21
22. HTML + RDF = Common Web Data Formats
OSLC
Web of Documents Web of Data
22
23. Schemas for Data Interoperability
schema.org Requirements
PLM
OSLC
domain-specific
standards (e.g.
for
Requirements)
OSLC
Web of Documents Web of Data
23
25. Links for Data Integration
URL1
URL2
URL3
OSLC
Requirements PLM ERPFacebook Server Wikipedia Server Blog Server
Link Link
Web of Documents Web of Data
URL1
URL2
URL3
Link Link
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26. Mashup Applications
Equal access to
information - more
competition amongst
data management
solutions
Search Visualize
(e.g Google, Bing) (e.g Chrome, Firefox) (e.g. IBM Lifecycle Query Engine and Mentor
Graphics Context)
Web of Documents Web of Data
26
URL1
URL2
URL3
Facebook Server Wikipedia Server Blog Server
Link Link
OSLC
Requirements PLM ERP
URL1
URL2
URL3
Link Link
Search Visualize
30. Mashup Applications for AI
Equal access to information -> more data available
to AI algorithms -> more interesting AI results
AI for Generative Design
30
CAD Simulation Manufacturing GraphDB Spark Elasticsearch
URL4
URL5
URL6
Link Link
URL1
URL2
URL3
Link Link
31. Private/public
Data Web
Distributed
Data Silos
Data
Repository 1
Data
Repository 2
Data
Repository 3
RDF Link Link RDF
Mashup
Application
Challenge
Scalability
31
What happens if the
private data Web
consists of 10 billion
resources? Can you still
query it?
Solution: use scalable big
data solutions used for
example by Google and
Amazon (e.g.
Elasticsearch, Amazon
Neptune)
32. Private/public
Data Web
Distributed
Data Silos
Data
Repository 1
Data
Repository 2
Data
Repository 3
RDF Link Link RDF
Mashup
Application
Challenge
Global
Configuration
Management
32
Which version of a
resource is linked with
which version of the
linked resource? Can you
do version management
at a global level?
Solution: use OSLC
Config management
standard for global
version management
33. Private/public
Data Web
Distributed
Data Silos
Data
Repository 1
Data
Repository 2
Data
Repository 3
RDF Link Link RDF
Mashup
Application
Challenge
Security
33
How can I make sure that
certain resources can
only be accessed by
certain users? How can
the access management
be more secure?
Solution: data access
management at a global
level + blockchain to
record who gets access to
what
34. We offer consulting services:
● Create OSLC APIs for software applications and data stores not supporting
OSLC natively
● Create integrations for OSLC-enabled applications (e.g. IBM DNG)
● Create mashup applications for OSLC data
● Offer OSLC training to developers and project managers
What does Koneksys do?
34
35. We perform internal research to address the challenges of future OSLC-based
mashup applications:
● Running queries on OSLC data with Spark GraphFrames
(https://github.com/koneksys/SPARQL_to_GraphFrames )
● Configuration management of OSLC data
(https://github.com/koneksys/Git4RDF )
● Managing information in the blockchain using smart contracts
(https://github.com/koneksys/Blockchain4LinkedData )
What does Koneksys do?
35
36. We help grow the OSLC community:
● Releasing open-source OSLC solutions (https://github.com/ld4mbse +
https://github.com/oslc/ )
● Creating new OSLC web site (http://oslc.co/ )
● Promoting OSLC at conferences (https://koneksys.com/blog/ )
What does Koneksys do?
36
37. Koneksys
Koneksys helps organizations create
data integration solutions using
● Linked Data
● Open Services for Lifecycle
Collaboration (OSLC)
● Big Data frameworks
● Graph Databases
Located in San Francisco. In business
since 2012.
Koneksys Clients
37
38. Open Services for Lifecycle Collaboration (OSLC)
Standards for servers hosting
data (Hypermedia REST API +
Linked Data REST API)
Standards for web-based data
interoperability
Adopted so far mainly for
Application Lifecycle
Management (ALM), systems
and requirements engineering
Open Community
38
Data
OSLC Adapter (Data
Web Server)
REST API (HTTP)
Linked Data (RDF)
Different Data Formats
XML, JSON, CSV, binary
Different Data Models
Relational, Graph, Document
Different Data IDs
integer, path, guid
Different APIs
Java, REST, query languages
Standardized
Web API
40. We need you to help promote OSLC!
New OSLC Web site: http://oslc.co/
Adding your company logo to the list of supporters on the web site helps the OSLC
community grow
If end user organizations show support for OSLC, then vendors, consultants, and
developers will offer more support for OSLC
40