1. Data as a Service – Concepts, Design &
Implementation, and Ecosystems
Hong-Linh Truong
Distributed Systems Group,
Vienna University of Technology
truong@dsg.tuwien.ac.at
http://dsg.tuwien.ac.at/staff/truong
1ASE Summer 2014
Advanced Services Engineering,
Summer 2014
Advanced Services Engineering,
Summer 2014

2. Outline
 Data provisioning and data service units
 Data-as-a-Service concepts
 DaaS design and implementation
 DaaS ecosystems
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3. Data versus data assets
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Data
Data
Assets
Data
management
and
provisioning
Data concerns
Data
collection,
assessment
and
enrichment

4. Data provisioning activities and
issues
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Collect
• Data sources
• Ownership
• License
• Quality
assessment
and
enrichment
Store
• Query and
backup
capabilities
• Local versus
cloud,
distributed
versus
centralized
storage
Access
• Interface
• Public versus
private
access
• Access
granularity
• Pricing and
licensing
model
Utilize
• Alone or in
combination
with other
data sources
• Redistribution
• Updates
Non-exhausive list! Add your own issues!
Provisioning Models

5. Stakeholders in data provisioning
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Data
Data Provider
• People
(individual/crowds/org
anization)
• Software, Things
Data Provider
• People
(individual/crowds/org
anization)
• Software, Things
Service Provider
• Software and people
Service Provider
• Software and people
Data Consumer
• People, Software,
Things
Data Consumer
• People, Software,
Things
Data Aggregator/Integrator
• Software
• People + software
Data Aggregator/Integrator
• Software
• People + software
Data Assessment
• Software and
people
Data Assessment
• Software and
people
Stakeholder classes can be further divided!
Domain-specific versus domain-independent functions

6. Recall – Service Unit
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Service
model
Unit
Concept
Service
unit
„basic
component“/“basic
function“ modeling
and description
Consumption,
ownership,
provisioning, price, etc.
What about service units providing data?What about service units providing data?

7. Data service unit
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Service
model
Unit
Concept
Data
service
unit
Data
 Can be used for private
or public
 Can be elastic or not
What about the
granularity of
the unit?
What about the
granularity of
the unit?

8. Data service units in clouds/internet
 Provide data capabilities rather than provide
computation or software capabilities
 Providing data in clouds/internet is an increasing
trend
 In both business and e-science environments
 Bio data, weather data, company balance
sheets, etc., via Web services
 Now often in a combination of data + analytics
atop the data
 Reasons: economic benefits, performance, service
ecosystems
8ASE Summer 2014

9. Data service unitData service unit
9
Data service units in
clouds/internet
datadata
Internet/CloudInternet/Cloud
Data service unitData service unit
People
data
Data service unitData service unit
Things
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data data

10. SO DATA SERVICE UNIT IS
BIG OR SMALL? PROVIDING
REALTIME OR STATIC DATA?
Discussion time
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11. 11
NIST Cloud definitions
“This cloud model promotes availability and is
composed of five essential characteristics,
three service models, and four deployment
models.”
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Source: NIST Definition of Cloud Computing v15, http://csrc.nist.gov/groups/SNS/cloud-computing/cloud-def-v15.docSource: NIST Definition of Cloud Computing v15, http://csrc.nist.gov/groups/SNS/cloud-computing/cloud-def-v15.doc

12. Data as a Service -- characteristics
 On-demand self-service
 Capabilities to provision data at different granularities
 Resource pooling
 Multiple types of data, big, static or near-realtime,raw data and
high-level information
 Broad network access
 Can be access from anywhere
 Rapid elasticity
 Easy to add/remove data sources
 Measured service
 Measuring, monitoring and publishing data concerns and usage
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Built atop NIST‘s definition

13. Data-as-a-Service – service modelsData-as-a-Service – service models
Data as a Service – service models
and deployment models
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Storage-as-a-Service
(Basic storage functions)
Storage-as-a-Service
(Basic storage functions)
Database-as-a-Service
(Structured/non-structured
querying systems)
Database-as-a-Service
(Structured/non-structured
querying systems)
Data publish/subcription
middleware as a service
Data publish/subcription
middleware as a service
Sensor-as-a-ServiceSensor-as-a-Service
Private/Public/Hybrid/Community CloudsPrivate/Public/Hybrid/Community Clouds
deploy

14. Examples of DaaS
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Xively Cloud Services™
https://xively.com/
Xively Cloud Services™
https://xively.com/

15. WHAT ELSE DO YOU THINK
CAN BE INCLUDED INTO DAAS
MODELS?
Discussion time
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16. DaaS design & implementation –
APIs
 Read-only DaaS versus CRUD DaaS APIs
 Service APIs versus Data APIs
 They are not the same wrt data/service
concerns
 SOAP versus REST
 Streaming data API
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17. DaaS design & implementation –
service provider vs data provider
 The DaaS provider is separated from the data
provider
17
DaaS
Consumer
DaaS
Sensor
DaaS
Consumer DaaS provider Data
provider
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18. Example: DaaS provider =! data
provider
18ASE Summer 2014

19. DaaS design & implementation –
structures
 DaaS and data providers have the right to
publish the data
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DaaS
• Service
APIs
• Data APIs
for the
whole
resource
Data
Resource
• Data APIs
for
particular
resources
• Data APIs
for data
items
Data Items
• Data APIs
for data
items
Three levels

20. 20
DaaS design & implementation –
structures (2)
Data
items
Data
items
Data
items
Data resourceData resource
Data
assets
Data resourceData resource Data resourceData resource
Data resourceData resourceData resourceData resource
Consumer
Consumer
DaaS
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21. DaaS design & implementation –
patterns for „turning data to DaaS“ (1)
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DaaSDaaSdatadata Build Data
Service
APIs
Deploy
Data
Service
Examples: using WSO2 data service

22. Storage/Database
-as-a-Service
Storage/Database
-as-a-Service
DaaS design & implementation –
patterns for „turning data to DaaS“ (2)
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datadata
Examples: using
Amazon S3
DaaSDaaS

23. Storage/Databa
se/Middleware
Storage/Databa
se/Middleware
DaaS design & implementation –
patterns for „turning data to DaaS“ (3)
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datadata
Examples:
using Crowd-
sourcing with
Pachube (the
predecessor of
Xively)
Things
One Thing  10000... Things
DaaSDaaS

24. Storage/Database/
Middleware
Storage/Database/
Middleware
DaaS design & implementation –
patterns for „turning data to DaaS“ (4)
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datadata
Examples: using Twitter
People
DaaSDaaS

25. ........
DaaS design & implementation –
not just „functional“ aspects (1)
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datadata DaaSDaaS.... data assetsdata assets
Data
concerns
Quality of
data
Ownership
Price
License ....
Enrichment
Cleansing
Profiling
Integration ...
Data Assessment
/Improvement
APIs, Querying, Data Management, etc.

26. DaaS design & implementation –
not just „functional“ aspects (2)
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Understand the DaaS ecosystem
Specifying, Evaluating and Provisioning Data
concerns and Data Contract
In follow-up
lectures

27. WHAT ARE OTHER PATTERNS
IN „TURNING DATA TO
DAAS“?
Discussion time
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28. DaaS ecosystems
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Data Assessment and Enrichment
Marco Comerio, Hong Linh Truong, Carlo Batini, Schahram Dustdar: Service-oriented data quality engineering and
data publishing in the cloud. SOCA 2010: 1-6
Marco Comerio, Hong Linh Truong, Carlo Batini, Schahram Dustdar: Service-oriented data quality engineering and
data publishing in the cloud. SOCA 2010: 1-6

29. Examples of service units in DaaS
ecosystems
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Platforms/services Capabilities
Strikeiron clean, verify and validate data.
Jigsaw clean, verify and validate
business contact.
PostcodeAnywhere capture, clean, validate
and enrich business data.
Trillium Software Quality clean and standardize data
Uniserv Data Quality Solution X profile and clean data
Adeptia Integration Solution integrate data
Marco Comerio, Hong Linh Truong, Carlo Batini, Schahram Dustdar: Service-oriented data quality engineering and
data publishing in the cloud. SOCA 2010: 1-6
Marco Comerio, Hong Linh Truong, Carlo Batini, Schahram Dustdar: Service-oriented data quality engineering and
data publishing in the cloud. SOCA 2010: 1-6

30. DaaS ecosystem –
profiling/enriching example
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http://www.strikeiron.com/

31. Cloud-based conceptual architecture
for data quality and enrichment
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Marco Comerio, Hong Linh Truong, Carlo Batini, Schahram Dustdar: Service-oriented data quality engineering and
data publishing in the cloud. SOCA 2010: 1-6
Marco Comerio, Hong Linh Truong, Carlo Batini, Schahram Dustdar: Service-oriented data quality engineering and
data publishing in the cloud. SOCA 2010: 1-6

32. Data Enrichment using Web data
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Source: Gomadam, K.; Yeh,
P.Z.; Verma, K.; Miller, J.A.,
"Data Enrichment Using Web
APIs," Services Economics
(SE), 2012 IEEE First
International Conference on ,
vol., no., pp.46,53, 24-29 June
2012
Source: Gomadam, K.; Yeh,
P.Z.; Verma, K.; Miller, J.A.,
"Data Enrichment Using Web
APIs," Services Economics
(SE), 2012 IEEE First
International Conference on ,
vol., no., pp.46,53, 24-29 June
2012

33. WHY DO YOU NEED TO STUDY
DAAS CONCEPTS, DESIGN
AND IMPLEMENTATION, AND
ECOSYSTEMS?
Discussion time
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34. Some conceptual questions
 What are the relationshipes between „data service unit“
and DaaS?
 „Data service unit“ versus DaaS versus Data
Marketplace?
 The unit concept supports „composability“
 What does it mean „composability“ of data service
units? multiple data service units or multiple data
resources?
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With the current trend on the API Management: service
providers focus on management of their API metadata
and lifecycle, is the concept of „service unit“ relevant to
API management? What are the relationships between
service units and APIs
With the current trend on the API Management: service
providers focus on management of their API metadata
and lifecycle, is the concept of „service unit“ relevant to
API management? What are the relationships between
service units and APIs

35. Exercises
 Read mentioned papers
 Check characteristics, service models and
deployment models of mentioned DaaS (and
find out more)
 Identify services in the ecosystem of some DaaS
 Write small programs to test public DaaS, such
as Xively, Microsoft Azure and Infochimps
 Turn some data to DaaS using existing tools
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36. 36
Thanks for
your attention
Hong-Linh Truong
Distributed Systems Group
Vienna University of Technology
truong@dsg.tuwien.ac.at
http://dsg.tuwien.ac.at/staff/truong
ASE Summer 2014