On Analyzing and Developing Data Contracts in Cloud-based Data Marketplaces

On Analyzing and Developing Data Contracts in Cloud-based Data Marketplaces

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  On Analyzing andDeveloping 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
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  Outline  Background andmotivation  Analysis of data contracts  Model of abstract data contracts  Experiments APSCC 2011, 12 Dec, 2011, Jeju, Korean 2
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  Background  The riseof 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 APSCC 2011, 12 Dec, 2011, Jeju, Korean 3
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  Motivation  Well-researched contractsfor 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. APSCC 2011, 12 Dec, 2011, Jeju, Korean 4
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  Study of maindata 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 APSCC 2011, 12 Dec, 2011, Jeju, Korean 5
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  Data contract study APSCC2011, 12 Dec, 2011, Jeju, Korean 6
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  Developing data contractsin 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 APSCC 2011, 12 Dec, 2011, Jeju, Korean 7
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  Community view ondata 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 APSCC 2011, 12 Dec, 2011, Jeju, Korean 8
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  Representing data contractterms  Contract term: (termName,termValue)  Term name: common terms or user-specific terms  Term value: a single value, a set, or a range APSCC 2011, 12 Dec, 2011, Jeju, Korean 9
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  Structuring abstract datacontracts Concrete data generates contracts can be in RDF, XML or JSON Use Identifiers and Tags for identifying and searches APSCC 2011, 12 Dec, 2011, Jeju, Korean 10
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  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 APSCC 2011, 12 Dec, 2011, Jeju, Korean 11
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  Prototype  RDF forrepresenting term categories, term names, term values, units  Allegro Graph for storing contract knowledge APSCC 2011, 12 Dec, 2011, Jeju, Korean 12
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  Illustrating examples  Alarge 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 APSCC 2011, 12 Dec, 2011, Jeju, Korean 13
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  Existing common knowledge about Open Data Commons APSCC 2011, 12 Dec, 2011, Jeju, Korean 14
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  Step 2: provideOpenBuildingCO2 OpenBuildingCO2 by OpenGov for modifying quality of government data data and data right Data contract for green building data APSCC 2011, 12 Dec, 2011, Jeju, Korean 15
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  Experiments – composingdata contract terms APSCC 2011, 12 Dec, 2011, Jeju, Korean 16
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  Conclusions and futurework  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] APSCC 2011, 12 Dec, 2011, Jeju, Korean 17
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  Thanks for yourattention! Hong-Linh Truong Distributed Systems Group Vienna University of Technology Austria truong@infosys.tuwien.ac.at http://www.infosys.tuwien.ac.at/staff/truong APSCC 2011, 12 Dec, 2011, Jeju, Korean 18