1. Evaluating and Utilizing Data 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 5
Advanced Services Engineering,
Summer 2014, Lecture 5

2. Outline
 Data concern-aware DaaS service engineering
 Data concern evaluation
 Data concern publishing
 A Proof-of-concept: QoD Framework
 Issues in utilizing data concerns
ASE Summer 2014 2

3. ........
Recall -- DaaS Concerns
ASE Summer 2014 3
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.

4. 4
Recall -- DaaS design &
implementation
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
ASE Summer 2014

5. HOW TO EVALUATE DATA
CONCENRS FOR DATA
ASSETS IN DAAS?
ASE Summer 2014 5

6. Patterns for „turning data to DaaS“
ASE Summer 2014 6
Storage/Database
-as-a-Service
Storage/Database
-as-a-Service
datadata DaaSDaaS
Storage/Databa
se/Middleware
Storage/Databa
se/Middleware
datadata
Things
DaaSDaaS
Storage/Database/
Middleware
Storage/Database/
Middleware
datadata
People
DaaSDaaS
DaaSDaaSdatadata Build Data
Service
APIs
Deploy
Data
Service

7. Data-related activities
ASE Summer 2014 7
Wrapping
data
Publishing DaaS
interface
Typical activities for data wrapping and publishing
Typical activities for data updating & retrieval
Updating
data
Selecting
data
datadata
Provisioning
data

8. Wrapping data
 (Relational) database
 (Storage of ) Files
 Streams of events (including attached
information)
 Service interfaces are different
 Update mechanisms are different
ASE Summer 2014 8

9. Typical data concern evaluation
ASE Summer 2014 9
Evaluating data
concerns
Evaluating data
concerns
Describing data
concerns
Describing data
concerns
Data Concerns
Evaluation Tools
Data Concerns
Representation Models
Populating data
concerns
Populating data
concerns
Publishing services
What do we need in order to perform these activities?

10. 10
Data concern-aware DaaS
engineering process Typical activities
for data wrapping
and publishing
Typical activities
for data updating &
retrieval
ASE Summer 2014
Hong Linh Truong, Schahram Dustdar: On Evaluating and Publishing
Data Concerns for Data as a Service. APSCC 2010: 363-370
Hong Linh Truong, Schahram Dustdar: On Evaluating and Publishing
Data Concerns for Data as a Service. APSCC 2010: 363-370

11. DaaS service operationDaaS service operation
Wrapping, selecting, and updating
data in DaaS (1)
11ASE Summer 2014
Processing
parameter
Processing
parameter
Mapping parameters to
data queries parameter
Query content of
data resources
Mapping and
returning results
Mapping and
returning results
Mapping parameters to
metadata queries
Mapping parameters to
metadata queries
Querying metadata of
data resources
Querying metadata of
data resources
Data
Consumer
Data
Consumer
different strategies for structured data and unstructured data

12. Wrapping, selecting, and updating
data in DaaS (2)
 Different techniques exist for wrapping,
selecting, updating and retrieving data
 How generic data concern evaluation and
publishing techniques can be integrated with
these techniques?
12ASE Summer 2014

13. WHICH TYPES OF DATA ARE NEEDED FOR
EVALUATING DATA CONCERNS?
WHAT IS THE IMPACT OF DATA
PROVISIONING MODELS (OFFLINE
VERSUS NEAR-REALTIME) ON CONCERN
EVALUATION/PUBLISHING?
Discussion
ASE Summer 2014 13

14. Evaluating data concerns – the
three important points
14
• At which level the
evaluation is performed?
evaluation
scope
• When the evaluation is
done?
evaluation
modes
• How the evaluation tool
is invoked?
integration
model
ASE Summer 2014
Hong Linh Truong, Schahram Dustdar: On Evaluating and Publishing Data Concerns for Data as a Service. APSCC
2010: 363-370
Hong Linh Truong, Schahram Dustdar: On Evaluating and Publishing Data Concerns for Data as a Service. APSCC
2010: 363-370

15. Evaluating data concerns –
evaluation scopes
 Three scopes
 data resource
 DaaS operations
 DaaS as a whole
15
Why multiple evaluation scopes make sense?
enable fine-grained evaluationenable fine-grained evaluation
ASE Summer 2014

16. Evaluating data concerns –
evaluation modes
 Off-line
 before the access to data
 On-the-fly
 when the data is requested
16
Why multiple evaluation modes make sense?
suitable for different types of datasuitable for different types of data
ASE Summer 2014

17. Evaluating data concerns –
integration modes
 Push and pull data concerns
 Pass-by-value versus pass-by-reference to data
concerns evaluation tools
17
Why multiple integration modes make sense?
suitable for different tool integration strategiessuitable for different tool integration strategies
ASE Summer 2014

18. Evaluating data concerns – some
patterns (1)
18
Pull, pass-by-referencesPull, pass-by-references
ASE Summer 2014

19. Evaluating data concerns – some
patterns (2)
19
Pull, pass-by-valuesPull, pass-by-values
ASE Summer 2014

20. Evaluating data concerns – some
patterns (3)
20
Push, pass-by-values (1)Push, pass-by-values (1)
ASE Summer 2014

21. Evaluating data concerns – some
patterns (4)
21
Push, pass-by-values (2)Push, pass-by-values (2)
ASE Summer 2014

22. Evaluation Tool – Internal Software
components
 Self-developed or third-party software
components for evaluation tool
 Advantages
 Tightly couple integration  performance, security,
data compliance
 Customization
 Disadvantages
 Usually cannot be integrated with other features
(e.g., data enrichment)
 Costly (e.g., what if we do not need them)
ASE Summer 2014 22

23. Evaluation tool – using cloud
services
 Evaluation features are provided by cloud
services
 Several implementations
 Informatica Cloud Data Quality Web Services, StrikeIron,
 Advantages
 Pay-per-use, combined features
 Disadvantages
 Features are limited (with certain types of data)
 Performance issues with large-scale data
 Data compliance and security assurance
ASE Summer 2014 23

24. Evaluation Tool -- using human
computation capabilities
 Professionals and Crowds can act as data
concerns evaluators
 For complex quality assessment that cannot be done by
software
 Issues
 Subjective evaluation
 Performance
 Limited type of data (e.g., images, documents, etc.)
ASE Summer 2014 24
Michael Reiter, Uwe Breitenbücher, Schahram Dustdar, Dimka Karastoyanova, Frank Leymann, Hong Linh Truong: A Novel
Framework for Monitoring and Analyzing Quality of Data in Simulation Workflows. eScience 2011: 105-112
Maribel Acosta, Amrapali Zaveri, Elena Simperl, Dimitris Kontokostas, Sören Auer, Jens Lehmann: Crowdsourcing Linked
Data Quality Assessment. International Semantic Web Conference (2) 2013: 260-276
Óscar Figuerola Salas, Velibor Adzic, Akash Shah, and Hari Kalva. 2013. Assessing internet video quality using
crowdsourcing. In Proceedings of the 2nd ACM international workshop on Crowdsourcing for multimedia (CrowdMM '13).
ACM, New York, NY, USA, 23-28. DOI=10.1145/2506364.2506366 http://doi.acm.org/10.1145/2506364.2506366
Michael Reiter, Uwe Breitenbücher, Schahram Dustdar, Dimka Karastoyanova, Frank Leymann, Hong Linh Truong: A Novel
Framework for Monitoring and Analyzing Quality of Data in Simulation Workflows. eScience 2011: 105-112
Maribel Acosta, Amrapali Zaveri, Elena Simperl, Dimitris Kontokostas, Sören Auer, Jens Lehmann: Crowdsourcing Linked
Data Quality Assessment. International Semantic Web Conference (2) 2013: 260-276
Óscar Figuerola Salas, Velibor Adzic, Akash Shah, and Hari Kalva. 2013. Assessing internet video quality using
crowdsourcing. In Proceedings of the 2nd ACM international workshop on Crowdsourcing for multimedia (CrowdMM '13).
ACM, New York, NY, USA, 23-28. DOI=10.1145/2506364.2506366 http://doi.acm.org/10.1145/2506364.2506366

25. BASED ON WHICH CRITERIA, AN EVALUATION
SCOPE, EVALUATION MODE OR INTEGRATION
MODE IS SELECTED?
Discussion time
ASE Summer 2014 25
WHICH ARE OTHER COMPONENTS INTERACTING
WITH EVALUATION TOOLS?
WHY DO WE NOT REALLY DISCUSS THE
IMPLEMENTATION OF EVALUATION TOOLS?

26. Publishing data concern
information (1)
 Off-line publishing of data concerns
 suitable for static data concerns
 the publishing of data concerns of a data
resource is separated from the service
operation which provides the access to the
data resource
ASE Summer 2014 26

27. Publishing data concern
information (2)
 On-the-fly publishing of data concerns
 associating concerns with retrieved data
resources
 the resulting data resources (e.g., via queries)
are annotated with data concerns evaluated
by data concerns evaluation tools.
 suitable for providing dynamic data concerns
ASE Summer 2014 27

28. 28
Publishing data concern
information (3)
 On-the-fly publishing of data concerns through
queries
 the use of different service operation
parameters to query data concerns of data
resources
 suitable for validating data concerns before
accessing data resources
ASE Summer 2014

29. WHAT ARE THE RELATIONSHIPS BETWEEN
CONCERN EVALUATION AND PUBLISHING
WHEN DATA IS DYNAMICALLY UPDATED?
Discussion time
ASE Summer 2014 29

30. How do we utilize the data concern-
aware service engineering process?
 Using this model we can determine and publish
several concerns
 Our “a proof-of-concept”
 A framework for evaluating and publishing QoD of
DaaS
 A proof-of-concept implementation of data concern-
aware service engineering process
 Another example: model and publish privacy
concerns for DaaS [ECOWS 2010]
ASE Summer 2014 30
Michael Mrissa, Salah-Eddine Tbahriti, Hong-Linh Truong, "Privacy model and annotation for DaaS", The 8th European
Conference on Web Services (ECOWS 2010), (c)IEEE Computer Society, 1-3 December, 2010, Ayia Napa, Cyprus
Michael Mrissa, Salah-Eddine Tbahriti, Hong-Linh Truong, "Privacy model and annotation for DaaS", The 8th European
Conference on Web Services (ECOWS 2010), (c)IEEE Computer Society, 1-3 December, 2010, Ayia Napa, Cyprus

31. 31
QoD framework (1)
 Pull QoD Evaluation Models for DaaS
 Pass-by-references and pass-by-value
 References of data resources: URI
 Values: any object
 Third-party data evaluation tools
ASE Summer 2014

32. 32
QoD framework (2)
ASE Summer 2014
http://www.infosys.tuwien.ac.at/prototype/SOD1/dataconcerns/http://www.infosys.tuwien.ac.at/prototype/SOD1/dataconcerns/

33. 33
QoD framework: publishing
concerns (1)
 Off-line data concern
publishing
 a common data concern
publication specification
 a tool for providing data concerns
according to the specification
 supported by external service
information systems
ASE Summer 2014

34. QoD framework: publishing
concerns (2)
 On-the-fly querying data concerns associated with data
resources
 Using REST parameter convention
 Based on metric names in the data concern
specification
ASE Summer 2014 34
Hong Linh Truong, Schahram Dustdar, Andrea Maurino, Marco Comerio: Context, Quality and Relevance:
Dependencies and Impacts on RESTful Web Services Design. ICWE Workshops 2010: 347-359
Hong Linh Truong, Schahram Dustdar, Andrea Maurino, Marco Comerio: Context, Quality and Relevance:
Dependencies and Impacts on RESTful Web Services Design. ICWE Workshops 2010: 347-359

35. QoD framework: publishing
concerns (3)
 Specifying requests by using utilizing query parameters
the form of metricName=value
35
 Obtaining contex and quality by using context and quality
parameters without specifying value conditions
GET/resource?crq.accuracy="0.5"&crq.location=’’Europe”GET/resource?crq.accuracy="0.5"&crq.location=’’Europe”
curl http://localhost:8080/UNDataService/data/query/Population annual growth rate
(percent)?crq.qod
{”crq.qod” : {
”crq.dataelementcompleteness ”: 0.8654708520179372,
”crq.datasetcompleteness”: 0.7356502242152466,
...
}}
curl http://localhost:8080/UNDataService/data/query/Population annual growth rate
(percent)?crq.qod
{”crq.qod” : {
”crq.dataelementcompleteness ”: 0.8654708520179372,
”crq.datasetcompleteness”: 0.7356502242152466,
...
}}
ASE Summer 2014

36. 36
QoD framework: QoD monitoring
and composition
 QoD concerns monitoring and composition are
useful for the evaluation of aggregated data
resources
 Our approach
 Utilizing monitoring rules
 QoD metrics of data resources are passed to an rule
engine
 Rules are user-defined for monitoring and composing
QoD metrics
ASE Summer 2014

37. QoD framework experiments
 Implementation
 Java, JAX-RS/Jersey, Drools
 Utilizing UNDataAPI - www.undata-api.org
 XML data sets without QoD
 Illustrating examples: check data from 1990-
2009
 datasetcompleteness: the completeness of the list of
countries
 dataelementcompleteness: the completeness of data
elements in the list metrics
 RESTful services wrapping to UNDataAPI
ASE Summer 2014 37

38. 38
QoD framework experiment:
evaluating and annotating QoD
metrics
ASE Summer 2014

39. 39
QoD framework experiments:
publishing QoD with data
resources
ASE Summer 2014

40. 40
QoD framework experiments:
simple rules for monitoring and
composing QoD
ASE Summer 2014

41. HOW TO SCALE THE
EVALUATION?
Discussion time
ASE Summer 2014 41

42. ISSUES IN UTILIZING DATA
CONCERNS
ASE Summer 2014 42

43. Elasticity
 If data does not fit for a purpose, because data
concerns do not meet the requirement from the
consumer
 DaaS may enrich the data,
 The consumer may switch to another DaaS
 The consumer may combine data from different
DaaSs
 The consumer may combine data from a DaaS with
its own data
 Elasticity of data and data concerns
ASE Summer 2014 43

44. Data fits to your purpose
 Data concern measurement
 They are determined from the data
 Whether they fit to your application is dependent on
application contexts
 Data concern interpretation
 Context-specific interpretation
 The same type of data with the same set of concern
measurements but might not fit for the same application at
different times/contexts
 Application-specific treatment!
 Strongly related to data elasticity
ASE Summer 2014 44

45. Exercises
 Read mentioned papers
 Identify and analyze the relationships between
data concerns evaluation tools and types of data
 Analyze trade-offs between on-line and off-line
evaluation and when we can combine them
 Analyze how to utilize evaluated data concerns
for optimizing data compositions
 Analyze situations when software cannot be
used to evaluate data concerns
ASE Summer 2014 45

46. 46
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