1. Analyzing and Specifying Concerns for
DaaS
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, Lecture 4
Advanced Services Engineering,
Summer 2014, Lecture 4

2. Outline
 What are data concerns and why their are
important
 Issues in DaaS concerns
 Analysis and specification of DaaS concerns
 Broad issues in DaaS concerns provisioning
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3. ........
What are data concerns?
datadata DaaSDaaS.... data assetsdata assets
APIs, Querying, Data Management, etc.
Located
in US?
free?
price?
redistribution?
Service
quality?
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Quality of data? Privacy
problem?

4. ........
DaaS concerns
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datadata DaaSDaaS.... data assetsdata assets
Data
concerns
Quality of
data
Ownership
Price
License ....
APIs, Querying, Data Management, etc.
DaaS concerns include QoS, quality of data (QoD),
service licensing, data licensing, data governance, etc.
DaaS concerns include QoS, quality of data (QoD),
service licensing, data licensing, data governance, etc.

5. Why DaaS/data concerns are
important?
 Too much data returned to the
consumer/integrator are not good
 Results are returned without a clear usage and
ownership causing data compliance problems
 Consumers want to deal with dynamic changes
5
Ultimate goal: to provide relevant data with
acceptable constraints on data concerns in
different provisioning models
Ultimate goal: to provide relevant data with
acceptable constraints on data concerns in
different provisioning models
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6. Example: mashup (1)
 Composition of Yahoo! Boss News Search,
Google News Search , and Flickr
 recent news and high-qualified images, but free-
 of charge, related to "Haiti earthquake"
6
Hong Linh Truong, Marco Comerio, Andrea Maurino, Schahram Dustdar, Flavio De Paoli, Luca Panziera: On
Identifying and Reducing Irrelevant Information in Service Composition and Execution. WISE 2010: 52-66
Hong Linh Truong, Marco Comerio, Andrea Maurino, Schahram Dustdar, Flavio De Paoli, Luca Panziera: On
Identifying and Reducing Irrelevant Information in Service Composition and Execution. WISE 2010: 52-66
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7. 7
Example: mashup (2)
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8. 8
If the composer is aware of context
and quality parameters
 Possible mappings of context and quality
requirements
but it is a tedious task and hard to be automated and we
are not sure we have a correct mapping.
but it is a tedious task and hard to be automated and we
are not sure we have a correct mapping.
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9. Example: open data (1)
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10. 10
Example: open data (2)
 Retrieve big
datasets from
services for further
extraction,
transform or data
composition
activities
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Example: open data (3)
 Example: study the population growth and
literacy rate from 1990-2009 for all countries in
the world
 Without QoD: get datasets and perform mashup
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Example: open data (4)
 With QoD support:
 Population annual growth rate (percent):
 dataelementcompleteness= 0.8654708520179372,
datasetcompleteness=0.7356502242152466;
 Adult literacy rate (percent):
 dataelementcompleteness=0.5874439461883408
 datasetcompleteness=0.04349775784753363
Should we retrieve the data and perform data
composition?
Should we retrieve the data and perform data
composition?
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CountriesYear 1990 ... 2009
1
...
223
223
elements

13. 13
Example: smart environments
 Smart environments with several low level sensors:
 Recognize human activities: idle, relaxing, and cleaning
up,
 Provide context information for adaptive service
discovery and execution
 E.g., FP7 SM4All, FP7 EU OPPORTUNITY
 Virtual Sensor-as-a-Service provides human activities
 Sensors in smart cities for sustainability governance
 E.g., Xively, Pacific Controls
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14. 14
Example: smart environments (2)
PoC: Probability of Correctness
QoC: Quality of Context
VSS: Virtual Sensor Service
CMS: Context Management Service
CCS: Context Consumer Service
AC: Appliances Control (AC)
AM: Ambiance Management
Atif Manzoor, Hong Linh Truong, Christoph
Dorn, Schahram Dustdar: Service-centric
Inference and Utilization of Confidence on
Context. APSCC 2010: 11-18
Atif Manzoor, Hong Linh Truong, Christoph
Dorn, Schahram Dustdar: Service-centric
Inference and Utilization of Confidence on
Context. APSCC 2010: 11-18
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15. WHAT ARE OTHER CASES
WHERE DAAS CONCERNS
ARE IMPORTANT FOR?
Discussion time
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16. Issues on DaaS concerns (1)
 DaaS concern models
 Unstructured description of context, QoS and
quality of data (QoD)
 Different specifications and terminologies
 Mismatching semantics of information about
services and data concerns
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17. Issues on DaaS concerns (2)
 DaaS APIs
 No/Limited description of data and service
usage
 No API for retrieving quality and context
information
 No quality and context information associated
with requested data
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18. Issues on DaaS concerns (3)
 Evaluation techniques
 Missing evaluation of compatibility of context
and concerns for multiple DaaS and data
assets
 Missing evaluation techniques to filter
large/irrelevant data quantity
18
Require a „holistic integration“ of information models,
APIs and evaluation techniques for DaaS concerns!
Require a „holistic integration“ of information models,
APIs and evaluation techniques for DaaS concerns!
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19. 19
Solutions needed
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Developing techniques for context and DaaS concerns evaluation
On-the-fly data concerns
evaluation
Concerns compatibility evaluation
and composition
Developing context and DaaS concerns that can be accessed via open
APIs
APIs extension External DaaS information service
Developing meta-model and domain-dependent semantic
representations for quality and context information specifications
Reconciliation of DaaS concern
terms
Linked DaaS concerns models

20. WHY CONTEXT IS
IMPORTANT?
Discussion time
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21. DaaS concerns analysis and
specification
 Which concerns are important in which
situations?
 How to specify concerns?
21ASE Summer 2014
Hong Linh Truong, Schahram Dustdar On analyzing and specifying concerns for data as a service. APSCC 2009: 87-
94
Hong Linh Truong, Schahram Dustdar On analyzing and specifying concerns for data as a service. APSCC 2009: 87-
94

22. The importance of concerns in
DaaS consumer‘s view – data
governance
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Important factor, for example, the security and
privacy compliance, data distribution, and auditing
Storage/Database
-as-a-Service
Storage/Database
-as-a-Service
datadata DaaSDaaS
Data governance

23. The importance of concerns in DaaS
consumer‘s view – quality of data
Read-only DaaS
 Important factor for the
selection of DaaS.
 For example, the
accurary and
compleness of the data,
whether the data is up-to-
date
CRUD DaaS
 Expected some support
to control the quality of
the data in case the data
is offered to other
consumers
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24. The importance of concerns in
DaaS consumer‘s view– data and
service usage
Read-only DaaS
 Important factor, in
particular, price, data
and service APIs
licensing, law
enforcement, and
Intellectual Property
rights
CRUD DaaS
 Important factor, in
paricular, price, service
APIs licensing, and law
enforcement
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25. The importance of concerns in
DaaS consumer‘s view – quality of
service
Read-only DaaS
Important factor, in
particular availability and
response time
CRUD Daas
Important factor, in
particular, availability,
response time,
dependability, and security
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26. The importance of concerns in DaaS
consumer‘s view– service context
Read-only DaaS
Useful factor, such as
classification and service
type (REST, SOAP),
location
CRUD DaaS
Important factor, e.g.
location (for regulation
compliance) and versioning
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27. WHAT ARE OTHER
IMPORTANT ISSUES? ADD
YOUR FINDING!
Discussion time
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28. Conceptual model for DaaS
concerns and contracts
28ASE Summer 2014

29. Capability concerns
Data Quality capabilities
Based on well-established research on data quality
Timelineness, uptodate, free-of-error, cleaning, consistency,
completeness, domain-specific metrics, etc.
We mainly support the specification of QoD metrics for the whole
DaaS but possible to extend to the service operation level
Data Security/Privacy capabilities
Data protection within DaaS, e.g. encryption, sensitive data
filtering, and data privacy
Many terms are based on the W3C P3P
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30. Capability concerns (2)
Auditing capabilities
Logging, reporting (e.g., daily, weekly, and monthly),
and warning
Support system maintenance, SLA monitoring, billing,
and taxation
Data lifecycle
Backup/recovery, distribution (e.g., a service is in
Europe but data is stored in US), and disposition
Support system maintenance but also regulation on
data
30ASE Summer 2014

31. Capability concerns (3)
Data and service license
Usage permission: for data (distribution, transfer,
personal use, etc.) and for service APIs (adaptation,
composition, derivation, etc.)
We utilize some terms from ODRL/ODRL-S
Copyrights
Liability: e.g., who is reponsible for the loss due to a
network disruption?
Law enforcement (e.g., US or European court)
Domain specific Intellectural property rights
31ASE Summer 2014

32. Data source concerns
A DaaS may utilize data from many sources.
Similar DaaSs may utilize data from the same source
Data source properties
Name, e.g. ddfFlus or DataFlux
Size
Timespan: the duration of collected data
Update Frequency: how offen the data is updated
etc
32ASE Summer 2014

33. Service context concerns
Location:
Selecting a DaaS in Amazon US Zone or European Zone?
Service Type: REST or SOAP?
Level of Service
Service Classification
Based on UNSPSC Code Classification Services
Data Classification
Service/data versioning
33ASE Summer 2014

34. 34
XML Diagram for the DaaS
capability specification
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35. 35
XML Diagram for DaaS specification
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36. 36
Implementation (1)
Check http://www.infosys.tuwien.ac.at/prototyp/SOD1/dataconcernsCheck http://www.infosys.tuwien.ac.at/prototyp/SOD1/dataconcerns
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37. 37
Implementation (2)
 Data privacy concerns are annotated with WSDL
and MicroWSMO
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38. 38
Implementation (3)
 Joint work with
Michael Mrissa, Salah-Eddine Tbahriti, Hong Linh
Truong: Privacy Model and Annotation for
DaaS. ECOWS 2010: 3-10
Michael Mrissa, Salah-Eddine Tbahriti, Hong Linh
Truong: Privacy Model and Annotation for
DaaS. ECOWS 2010: 3-10
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39. HOW TO MODEL DOMAIN-
SPECIFIC DATA CONCERNS?
HOW TO DEAL WITH THE BIG
SCALE (BIG DATA)?
Discussion time
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40. Recall -- 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

41. 41
Populating DaaS concerns
DaaS
Concerns
evaluate, specify,
publish and manage
specify, select,
monitor, evaluate
monitor and
evaluate
The role of stakeholders in the most trivial view
Data
Aggregator/Integrator
Data
Consumer
Data
Assessment
Service Provider
Data Provider
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42. Support DaaS concerns selection
42
Service Information
Management
Service
service/data
information, including
concerns
DeXIN
Data
Consumer
External
sources
SECO2
1. Muhammad Intizar Ali, Reinhard Pichler, Hong Linh Truong, Schahram Dustdar: Data Concern Aware Querying
for the Integration of Data Services. ICEIS (1) 2011: 111-119
2. Marco Comerio, Hong Linh Truong, Flavio De Paoli, Schahram Dustdar: Evaluating Contract Compatibility for
Service Composition in the SeCO2 Framework. ICSOC/ServiceWave 2009: 221-236
1. Muhammad Intizar Ali, Reinhard Pichler, Hong Linh Truong, Schahram Dustdar: Data Concern Aware Querying
for the Integration of Data Services. ICEIS (1) 2011: 111-119
2. Marco Comerio, Hong Linh Truong, Flavio De Paoli, Schahram Dustdar: Evaluating Contract Compatibility for
Service Composition in the SeCO2 Framework. ICSOC/ServiceWave 2009: 221-236
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43. 43
From capability/context to
DaaS contract
43
Search
properties of
DaaSs
Define and
negotiate contract
terms
Contracts
DaaS Capabilities,
Context, Data
Source
Consumer-specific
concerns
A DaaS contract includes a set of generic, data-
specific and service-specific conditions established
based on concerns (see Lecture 6)
A DaaS contract includes a set of generic, data-
specific and service-specific conditions established
based on concerns (see Lecture 6)
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44. WHAT CAN WE DO MORE
WITH INFORMATION ABOUT
DAAS CONCERNS?
Discussion time
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45. Providing data concerns
 Importance issue for DaaS service/data
providers
 How do data concerns provisioning models influence
service provisioning models, service execution and
management?
 Some complex tradeoffs
 Increase quality of data
 increase computational effort ?
 reduce the performance ?
 Providing data concerns
 Need more resources, Need more data
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46. Data concerns in multi-dimensional
elasticity
Simple
dependency
flows (increase nr. of services)
(increase) (increase response time)
(increase cost)
How do we maintain
our systems to deal
with such complex
dependencies?
How do we maintain
our systems to deal
with such complex
dependencies?
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47. Exercises
 Read mentioned papers
 Visit DaaS mentioned in previous lectures
 Analyze existing DaaS concerns
 Examine how they specify and publish concerns
 Investigate possible concerns when merging
data from different types of DaaS
 Open government data and near-realtime data from
sensors
 Work on some DaaS concern dependency
scenarios
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48. 48
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