On Evaluating and Publishing Data Concerns for Data as a Service

On Evaluating and Publishing Data
Concerns for Data as a Service
Hong-Linh Truong and Schahram Dustdar

Distributed Systems Group, Vienna University of Technology

truong@infosys.tuwien.ac.at
http://www.infosys.tuwien.ac.at/Staff/truong

APSCC 2010, Hangzhou 9 Dec 2010 1

Overview

 Motivation and background
 Data concern-aware service engineering
process
 A framework for evaluating and publishing QoD
of DaaS
 Experiments
 Conclusions and future work


The rise of DaaS
 Web services technologies and the cloud computing
model foster the concept of data/information as a service
(DaaS)
 Provide data capabilities rather than provide
computation or software
 Providing DaaS is an increasing trend
 In both business and e-science environments
 Bio data, weather data, company balance sheets,
etc., via Web services
 But data is associated with many data concerns
 Quality of data, privacy, licensing, etc.


Examples of DaaS
Source: http://www.undata-api.org/ Source:
http://www.strikeiron.com/Catalog/StrikeIronServices.aspx

Source:
http://docs.gnip.com/w/page/23722723/Introduction-to-Gnip
4

Motivation: the role of data
concerns Should we perform data
composition?

 Data consumers/data integrators need “data
concerns”
 to use data in a right way: Is the data good? Or free?
 to filter irrelevant results: avoid information
overloading
 to save processing time/energy and storage
 Both DaaS service and data providers need to
evaluate and provide data concerns

Motivation: service provider versus
data provider
 The DaaS service provider is separated from the
data provider
Consumer Service provider Data provider
quality1

DaaS quality2

Consumer
DaaS privacy1

DaaS
privacy2
Sensor

the lack of techniques and tools to deal with the
evaluation and publishing of data concerns for DaaS

Example: DaaS provider =! data
provider

Source: http://www.infochimps.org

Background: data resources
 Data items → data resources →
DaaS APIs → consumers
 DaaS and data providers have the
Data resource
right to publish the data
Data
items
Consumer
Service APIs
Data Data
DaaS items items

Consumer

Data resource Data resource

Data resource Data resource
SOAP/REST


Backgroud: diverse concerns
associated with service and data

Hong-Linh Truong, Schahram Dustdar "On Analyzing and Specifying Concerns for Data as a Service" , The 2009 Asia-Pacific Services Computing
Conference (IEEE APSCC 2009), (c) IEEE Computer Society, December 7-11, 2009, Biopolis, Singapore.
9

Data concern-aware service
engineering process Typical activities
for data wrapping
and publishing

Typical activities
for data updating &
retrieval


Wrapping, selecting, and updating
data in DaaS
 Typically different strategies for structured data and
unstructured data – not our main work
 We just reuse existing techniques in order to plug our data
concern evaluation and publishing techniques


Evaluating data concerns (1)
 Based on three concepts:
 evaluation scope, evaluation modes and integration model
 Evaluation scopes – enable fine-grained evaluation
 Three scopes: data resource, service operation, and service as
a whole
 Evaluation modes – suitable for different types of data
 Off-line (before the access to data) and on-the-fly (when the data
is requested)
 Integration models – suitable for different tool integration strategies
 Push and pull data concerns
 Pass-by-value versus pass-by-reference to data concerns
evaluation tools


Evaluating data concerns (2)

Pull, pass-by-references Pull, pass-by-values

Push, pass-by-values


Publishing data concern
information
 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
 On-the-fly publishing of data concerns by 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
 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


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]
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


QoD framework: pull QoD
evaluation models for DaaS
 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


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


QoD framework: publishing
concerns (2)
 On-the-fly querying data concerns associated with data
resources
 Using our proposed REST parameter convention in
[Composable Web 2010]
 Based on metric names in the data concern
specification
 Specifying requests by using utilizing query parameters
the form of metricName=value
GET/resource?accuracy="0.5"&location=’’Europe”

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


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


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


Experiment: evaluating and
annotating QoD metrics

http://www.infosys.tuwien.ac.at/prototyp/SOD1/dataconcerns/

Experiments: publishing QoD with
data resources


Experiments: simple rules for
monitoring and composing QoD


Conclusions and future work
 A novel, generic data concern-aware service engineering
process for DaaS
 A proof-of-concept implementation for evaluating of
quality of data in REST-based DaaS
 but in principle other concerns can be supported
 more evaluation are needed
 Open research questions:
 how to deal with other concerns ?
 what are the trade-offs between on-line and off-line
evaluation ?
 how to utilize evaluated data concerns for optimizing
data compositions ?


Thanks for your attention!

Hong-Linh Truong
Distributed Systems Group
Vienna University of Technology
Austria

truong@infosys.tuwien.ac.at
http://www.infosys.tuwien.ac.at


On Evaluating and Publishing Data Concerns for Data as a Service

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