This presentation is part of the course "184.742 Advanced Services Engineering" at The Vienna University of Technology, in Winter Semester 2012. Check the course at: http://www.infosys.tuwien.ac.at/teaching/courses/ase/

1. Advanced Services Engineering,
WS 2012
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://www.infosys.tuwien.ac.at/staff/truong
ASE WS 2012 1

2. Outline
 Data provisioning and data service units
 Data-as-a-Service concepts
 DaaS design and implementation
 DaaS ecosystems
ASE WS 2012 2

3. Data versus data assets
Data
Data Assets
Data
collection,
assessment
and
enrichment
Data concerns
Data
management
and
provisioning
ASE WS 2012
3

4. Data provisioning activities and
issues
Collect Store Access Utilize
• Data sources • Query and • Interface • Alone or in
• Ownership backup • Public versus combination
• Quality capabilities private with other
assessment • Local versus access data sources
and cloud, • Access • Redistribution
enrichment distributed granularity
versus • Pricing and
centralized licensing
storage model
Non-exhausive list! Add your own issues!
ASE WS 2012 4

5. Stakeholders in data provisioning
Data Provider
• People
(individual/crowds/org
anization)
• Software, Things
Service Provider
Data Assessment • Software and people
• Software and
people
Data
Data Consumer
Data Aggregator/Integrator • People, Software,
• Software Things
• People + software
ASE WS 2012 5

6. Recall – Service Unit
Consumption,
ownership, Service
provisioning, price, etc. model
Service
unit
„basic
component“/“basic
function“ modeling Unit
Concept
and description
What about service units providing data?
ASE WS 2012 6

7. Data service unit
Service
model
Data
Data
service
unit
Unit
Concept
 Can be used for private
or public
 Can be elastic or not
ASE WS 2012 7

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
ASE WS 2012 8

9. Data service units in
clouds/internet
data data
data
Data service unit Data service unit Data service unit
data
People Things
Internet/Cloud
ASE WS 2012 9

10. Discussion time
SO DATA SERVICE UNIT IS
BIG OR SMALL? PROVIDING
REALTIME OR STATIC DATA?
ASE WS 2012 10

11. NIST Cloud definitions
“This cloud model promotes availability and is
composed of five essential characteristics,
three service models, and four deployment
models.”
Source: NIST Definition of Cloud Computing v15, http://csrc.nist.gov/groups/SNS/cloud-computing/cloud-def-v15.doc
ASE WS 2012 11

12. Data as a Service -- characteristics
Built atop NIST‘s definition
 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
ASE WS 2012 12

13. Data as a Service – service models
and deployment models
Data-as-a-Service – service models
Data publish/subcription Database-as-a-Service
middleware as a service (Structured/non-structured
querying systems)
Sensor-as-a-Service Storage-as-a-Service
(Basic storage functions)
deploy
Private/Public/Hybrid/Community Clouds
ASE WS 2012 13

14. Examples of DaaS
ASE WS 2012 14

15. Discussion time
WHAT ELSE DO YOU THINK
CAN BE INCLUDED INTO DAAS
MODELS?
ASE WS 2012 15

16. DaaS design & implementation –
APIs
 Read-only DaaS versus CRUD DaaS APIs
 Service APIs versus Data APIs
 They are not the same wrt concerns
 SOAP versus REST
Example: infochimps
ASE WS 2012 16

17. DaaS design & implementation –
service provider vs data provider
 The DaaS provider is separated from the data
provider
Consumer DaaS provider Data
provider
DaaS
Consumer
DaaS
DaaS
Sensor
ASE WS 2012 17

18. Example: DaaS provider =! data
provider
18

19. DaaS design & implementation –
structures
Three levels
DaaS Data Data Items
Resource
• Service • Data APIs • Data APIs
APIs for for data
• Data APIs particular items
for the resources
whole • Data APIs
resource for data
items
 DaaS and data providers have the right to
publish the data
ASE WS 2012 19

20. DaaS design & implementation –
structures (2)
Data resource
Data
items
Consumer
Data Data
Data items items
assets
Consumer
Data resource Data resource
Data resource Data resource
DaaS
ASE WS 2012 20

21. DaaS design & implementation –
patterns for „turning data to DaaS“ (1)
data Build Data Deploy DaaS
Service Data
APIs Service
Examples: using WSO2 data service
ASE WS 2012 21

22. DaaS design & implementation –
patterns for „turning data to DaaS“ (2)
Storage/Database
-as-a-Service
data DaaS
Examples: using
Amazon S3
ASE WS 2012 22

23. DaaS design & implementation –
patterns for „turning data to DaaS“ (3)
data
Storage/Databa
se/Middleware DaaS
Things
One thing  10000... things
Examples: using
COSM/Pachube
ASE WS 2012 23

24. DaaS design & implementation –
patterns for „turning data to DaaS“ (4)
data
Storage/Database/
Middleware DaaS
People
Examples: using Twitter
ASE WS 2012 24

25. DaaS design & implementation –
not just „functional“ aspects (1)
Profiling
Cleansing
Enrichment Integration ...
Data Assessment
/Improvement
data .... .... DaaS data assets
APIs, Querying, Data Management, etc.
Data
concerns
Quality of Ownership
data Price
License ....
ASE WS 2012 25

26. DaaS design & implementation –
not just „functional“ aspects (2)
Understand the DaaS ecosystem
Specifying, Evaluating and Provisioning Data
concerns and Data Contract
In follow-up
lectures
ASE WS 2012 26

27. Discussion time
WHAT ARE OTHER PATTERNS
IN „TURNING DATA TO
DAAS“?
ASE WS 2012 27

28. DaaS ecosystems
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
ASE WS 2012 28

29. Examples of service units in DaaS
ecosystems
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
ASE WS 2012 29

30. DaaS ecosystem –
profiling/enriching example
http://www.strikeiron.com/
ASE WS 2012 30

31. Cloud-based conceptual architecture
for data quality 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
ASE WS 2012 31

32. Discussion time
WHY DO YOU NEED TO STUDY
DAAS CONCEPTS, DESIGN
AND IMPLEMENTATION, AND
ECOSYSTEMS?
ASE WS 2012 32

33. 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 COSM/Pachube, Microsoft Azure and
Infochimps
 Turn some data to DaaS using existing tools
ASE WS 2012 33

34. Thanks for
your attention
Hong-Linh Truong
Distributed Systems Group
Vienna University of Technology
truong@dsg.tuwien.ac.at
http://www.infosys.tuwien.ac.at/staff/truong
ASE WS 2012 34