Stefano Nativi presents the RDA. Workshop title: Organising high-quality research data management services Workshop abstract: Open science needs high quality data management where researchers can create, use and share data according to well defined standards and practices. this is one of the pillars of Open Science. In the data management landscape we find quite a few organisations that aim at achieving this, however to get it right, a collaboration is called for where all can play a suitable role and present this in a consistent way to the researcher. The proposed workshop brings together representatives of standard organisation (RDA), eInfrastructures (EUDAT) and Libraries (LIBER) that together can organise the high quality data management for research. DAY 1 - PARALLEL SESSION 2 http://opensciencefair.eu/workshops/organising-high-quality-research-data-management-services

1. Brokering Services facilitating
interoperability and data management
provision
S. Nativi (CNR-IIA), P. Wittenburg (Max Planck Society),
B. Almas (Tufts University), J. Pearlman (FourBridges) and T. Weigel (DKRZ)
Athens, 6-7 Sep 2017

2. Genesis
launched in 2013 by the European
Commission, the US NSF and NIST,
and the Australian Government’s
Department of Innovation
Vision
Researchers and innovators openly
share data across technologies,
disciplines, and countries to address
the grand challenges of society
Mission
RDA builds the social and technical
bridges that enable open sharing of
data.
www.rd-alliance.org
@resdatall
CC BY-SA 4

3. 3
Cycle can be manually controlled or semi-automatically via pre-set pipelines.
Even in case of semi-automatic pipelines humans are close-in "designers“
(diagram is well-known in DFIG)
Observations
Experiments
Simulations
etc.
Data Fabric: Human Controlled Processing (HCP)

4. 4
Type-Triggered Automatic Processing (T-TAP)
Processing
services
Data Type
Registry
Data Events
Structured
Data Markets
result
New feature: cycles run highly autonomously -
precise steps depend on the types of data
entering the workflow
Researchers
are not in
direct control
some kind
of profile
matching
Brokering &
Mediation
services
scripts
adding new data
Data Federation
Agents
exposing
new DOs
Facilitating
Services/tools

5. 5
Principle Differences
HCP T-TAP
Human role procedural declarative
Aggregation designed profile driven
Events planned asynchronous
Mapping designed
brokered – third party services, it
becomes a business opportunity
Required
Metadata
• flexible in case of
human control
• detailed in case of WF
detailed
Typing
(semantics)
optional required
PID types optional required
DFT model optional required

6. 6
Global Digital Object Cloud (GDOC)
Abstraction level
Tier 4Tier 3Tier 2Tier 1

7. 7
The Potential role of Brokering services
Brokering & Mediation
services

8. 8
Conceptual
Object
Object
implemen
tation
Object
implemen
tation
Object
implemen
tationAllow different implementations of a
common conceptual object
Artifact
Artifact
Artifact
Artifact
Mediate among different artifact types
Type A Type B
Brokering to face Complexity

9. 9
Abstractions Vs Implementations
 Distinguish between two different types of Workflow
specifications/notations
 Abstract Workflow
 Executable Workflow
 Abstract Workflow (Business Process) specification
 Generated by Process Experts
 Based on abstract and well known object types
 Implementation/Technology independent
 Executable Workflow
 Generated by IT experts –e.g. Web Services experts
 Based on accessible object implementations –e.g. Web Services
 Technology dependent –e.g. workflow engines and related
languages

10. 10
Abstractions Vs Implementations
 PID and Object typing help fill the gap
 Brokering services (e.g. Data services brokering,
Processing services brokering) help fill the gap.
GAP
Abstract Business Process Executable Workflow(s)

11. 11
 Intermediation functionalities have been done for many
years by the diverse Communities to support their
applications –see end-to-end architecture
 With the advent of the Data Web, it‘s time to make it in an
automatic way –see T-TAP
 Pros
 More effective (i.e. multi-disciplinary),
 More sustainable and scalable,
 More re-usable
 Cons
 Need for a (new) governance and trust
 Need for a more typified approach
Thinking Outside the Box

12. 12
 In RDA the Data Fabric initiative is enabling Type-Triggering
automation to enable a more structured Data Market
 Need to move from the current infrastructure abstraction
level to a Global Digital Object Cloud
 Abstraction versus implementation complexities can be
addressed also by using brokering and intermediation
services
 In the Data Web era data brokering services should be
externalized (brokering-as-a-Service)
 RDA has addressed business models for such services
 Data Fabric, Type-Triggering automation, and Brokering
services can play an important role to enable the
collaboration among RDA, EUDAT and LIBER.
Conclusions

13. 13
Questions/Comments?