Currently, rich and diverse data types have been increasingly provided using the Data-as-a-Service (DaaS) model, a form of cloud computing services. However, data offered by DaaS are constrained by several data concerns that, if not automatically being reasoned properly, will lead to a wrong way of using them. In this paper, we support the assumption that data concerns should be explicitly modeled and specified in data contracts to support concern-aware data selection and utilization. Instead of relying on a specific definition of data contracts, we analyze contemporary data contracts and we present an abstract model for data contracts. Based on the abstract model, we propose several techniques for evaluating data contracts that can be integrated into data service selection and composition frameworks. We also illustrate our approach with some realworld scenarios.

1. On Analyzing and Developing Data
Contracts in Cloud-based Data
Marketplaces
Hong-Linh Truong1, G.R. Gangadharan2, Marco
Comerio3, Schahram Dustdar1, Flavio De Paoli3
1
Distributed Systems Group, Vienna University of Technology
2
Institute for Development & Research in Banking Technology (IDRBT), India
3
Department of Informatics, Systems and Communication, University of Milano
- Bicocca
truong@infosys.tuwien.ac.at
http://www.infosys.tuwien.ac.at/Staff/truong
APSCC 2011, 12 Dec, 2011, Jeju, Korean 1

2. Outline
 Background and motivation
 Analysis of data contracts
 Model of abstract data contracts
 Experiments
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3. Background
 The rise of data-as-a-service and data market
places
 Data contracts are important
 Give a clear information about data usage
 Have a remedy against the consumer where the
circumstances are such that the acts complained of do
not
 Limit the liability of data providers in case of failure of
the provided data;
 Specify information on data delivery, acceptance, and
payment
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4. Motivation
 Well-researched contracts for services but not for DaaS and
data marketplaces
 But service APIs != data APIs =! data assests
 Several open questions
 Right to use data? Quality of data in the data agreement? Search
based on data contract? Etc.
➔
Require extensible models
➔
Capture contractual terms for data contracts
➔
Support (semi-)automatic data service/data
selection techniques.
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5. Study of main data contract terms
 Data rights
 Derivation, Collection, Reproduction, Attribution
 Quality of Data (QoD)
 Not mentioned, Not clear how to establish QoD metrics
 Regulatory Compliance
 Sarbanes-Oxley, EU data protection directive, etc.
 Pricing model
 Different models, pricing for data APIs and for data assets
 Control and Relationship
 Evolution terms, support terms, limitation of liability, etc
Most information is in human-readable form
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6. Data contract study
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7. Developing data contracts in cloud-
based data marketplaces
 Our approach
 Follow community-based approach for data contract
 Propose generic structures to represent data
contract terms and abstract data contracts
 Develop frameworks for data contract applications
 Incorporate data contracts into data-as-a-service
description
 Develop data contract applications
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8. Community view on data contract
development
 Community users can develop:
 Term categories, term names, values, and units
 Rules for data contracts
 Common contract and contract fragments
Community users
=! novice users
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9. Representing data contract terms
 Contract term: (termName,termValue)
 Term name: common terms or user-specific terms
 Term value: a single value, a set, or a range
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10. Structuring abstract data contracts
Concrete data generates
contracts can be in
RDF, XML or JSON
Use Identifiers and
Tags for identifying
and searches
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11. Development of contract
applications
 Main applications:
 Data contract compatibility evaluation
 Data contract composition
 This paper does not deal with them but there are
some common steps
 Extract DCTermType in TermCategoryType
 Extact comprable terms from all contracts,
- e.g., dataRight: Derivation, Composition and Reproduction
 Use evaluation rules associated with DCTermType from
from rule repositories
 Execute rules by passing comparable terms to rules
 Aggregate results
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12. Prototype
 RDF for representing term
categories, term names, term
values, units
 Allegro Graph for storing
contract knowledge
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13. Illustrating examples
 A large sustainability monitoring data platform
shows how green buildings are
 Real-time total and per capita of CO2 emission
of monitored building
 Open government data about CO2 per capita at
national level
 We created contracts from
 Open Data Commons Attribution License
 Open Government License
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14. Existing
common
knowledge
about Open
Data
Commons
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15. Step 2: provide OpenBuildingCO2
OpenBuildingCO2 by OpenGov for
modifying quality of government data
data and data right
Data contract for green building data
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16. Experiments – composing data
contract terms
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17. Conclusions and future work
 Emerging data marketplaces and DaaS
 But lack of data contract support
 What constitutes data contracts has not been deeply
investigated
 Our contribution:
 Analysis of data contracts
 An approach and framework to support data contracts
 Future work
 Work on domain-specific applications
 Integrate data contracts with data agreement
exchange and data section and composition
frameworks
 Integrate data contracts to DEMODS [AINA 2012]
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18. 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/staff/truong
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