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Rich types of data offered by data as a service(DaaS) in the cloud are typically associated with differentand complex data concerns that DaaS service providers, dataproviders and data consumers must carefully examine andagree with before passing and utilizing data. Unlike serviceagreements, data agreements, reflecting conditions establishedon the basis of data concerns, between relevant stakeholdershave got little attention. However, as data concerns are complexand contextual, given the trend of mixing data sources byautomated techniques, such as data mashup, data agreementsmust be associated with data discovery, retrieval and utilization.Unfortunately, exchanging data agreements so far has not beenautomated and incorporated into service and data discovery andcomposition. In this paper, we analyze possible steps and proposeinteractions among data consumers, DaaS service providers anddata providers in exchanging data agreements. Based on that,we present a novel service for composing, managing, analyzingdata agreements for DaaS in cloud environments and datamarketplaces.
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Exchanging Data Agreements in the DaaS Model
Hong-Linh Truong1, Schahram Dustdar1, Joachim Goetze2, Tino Fleuren2, Paul Mueller2, Salah-Eddine Tbahriti3, Michael
Mrissa3, Chirine Ghedira3
1Distributed Systems Group, Vienna University of Technology2Department of Computer Science, University of Kaiserslautern
3Universite de Lyon, CNRS, Universite Lyon 1
[email protected]://www.infosys.tuwien.ac.at/Staff/truong
APSCC 2011, 12 Dec, 2011, Jeju, Korean
APSCC 2011, 12 Dec, 2011, Jeju, Korean
Outline
Background and motivation
Our approach
Techniques for data agreements exchange
Prototype and illustrative examples
APSCC 2011, 12 Dec, 2011, Jeju, Korean
Background
DaaS
Consumer
DaaS
Sensor
DaaS
Data consumer DaaS providerData marketplaces
Data provider
privacy1
quality1
quality2
privacy2
Emerging data-as-a-service and on-demand data provisioning in cloud-based data marketplaces Rich data concerns associated with data assets Dynamic interactions within data marketplaces
APSCC 2011, 12 Dec, 2011, Jeju, Korean
Motivation
Lack of models and protocols for data agreement in data marketplaces Constraints for data usage are not clear Inadequate data/service description → hindering data
selection and integration
Existing techniques are not adequate for dynamic data agreement exchange in data marketplaces
Our work: developing generic exchange models suitable for different ways of data provisioning in data marketplaces
APSCC 2011, 12 Dec, 2011, Jeju, Korean
Our approach
Develop metamodel for data agreement exchange
Investigate techniques for enriching and associating data assets with agreement terms
Develop interaction models for data agreement exchange
Develop Data Agreement Exchange as a Service (DAES)
APSCC 2011, 12 Dec, 2011, Jeju, Korean
Metamodel for data agreements
Different category of agreements Licensing,
privacy, quality of data
Extensions Languages Different types of
agreements Different
specifications
APSCC 2011, 12 Dec, 2011, Jeju, Korean
Associating data with data agreements
Solutions (a) directly inserting agreements into data assets
(b) providing two-step access to agreements and data assets
(c) linking data agreements to the description of DaaS
(d) linking data agreements to the message sent by DaaS
APSCC 2011, 12 Dec, 2011, Jeju, Korean
Possible interaction models for data enriched with data agreements
APSCC 2011, 12 Dec, 2011, Jeju, Korean
DAES – conceptual architecture
Jersey, JAX-RS Restful WS Weblogic
Using URIs to identify agreements
APSCC 2011, 12 Dec, 2011, Jeju, Korean
DAES – managed information
Specific applications: agreement creation, agreement validation, agreement compatibility analysis, agreement management
Implementation: Jersey, JAX-RS Restful WS Weblogic
APSCC 2011, 12 Dec, 2011, Jeju, Korean
Illustrating examples – insert agreement into data asset
A pay-per-use consumer uses dataAPI of DaaS search for data The consumer pays the use APIs Each call can return different types of data
Example with People Search in Infochimps
But a strong consequence for data service engineering techniques: dealing with elastic requirements!
APSCC 2011, 12 Dec, 2011, Jeju, Korean
Illustrating examples – link agreements to geospatial data
Domain-specific DaaS: different agreements for different data requests Vector data of geographic features via Web-Feature-Service (WFS)
Terrain elevation data via Web-Coverage Services (WCS)
APSCC 2011, 12 Dec, 2011, Jeju, Korean
Illustrating examples – link agreements to geospatial data
Consumers can interpret and reason if the data can be used for specific purposes
APSCC 2011, 12 Dec, 2011, Jeju, Korean
Illustrative examples – develop an app for policy compliance
Configuration
Results
APSCC 2011, 12 Dec, 2011, Jeju, Korean
Conclusions and future work
Dynamic data provisioning in DaaS/data marketplaces need new models and techniques for data agreement exchange
We present Models and interactions for data agreement exchange
Data Agreement Exchange Service (DAES)
Data agreement is not just “data licensing”!
Future work Integrate DAES with data contracts [another paper in
APSCC 11]
Integrate DAES with DEMODS ([AINA 2012])
APSCC 2011, 12 Dec, 2011, Jeju, Korean
Thanks for your attention!
Hong-Linh TruongDistributed Systems GroupVienna University of TechnologyAustria
[email protected]://www.infosys.tuwien.ac.at/staff/truong
