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


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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

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

  1. 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/truongAPSCC 2011, 12 Dec, 2011, Jeju, Korean 1
  2. 2. Outline Background and motivation Analysis of data contracts Model of abstract data contracts ExperimentsAPSCC 2011, 12 Dec, 2011, Jeju, Korean 2
  3. 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 paymentAPSCC 2011, 12 Dec, 2011, Jeju, Korean 3
  4. 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.APSCC 2011, 12 Dec, 2011, Jeju, Korean 4
  5. 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 formAPSCC 2011, 12 Dec, 2011, Jeju, Korean 5
  6. 6. Data contract studyAPSCC 2011, 12 Dec, 2011, Jeju, Korean 6
  7. 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 applicationsAPSCC 2011, 12 Dec, 2011, Jeju, Korean 7
  8. 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 usersAPSCC 2011, 12 Dec, 2011, Jeju, Korean 8
  9. 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 rangeAPSCC 2011, 12 Dec, 2011, Jeju, Korean 9
  10. 10. Structuring abstract data contracts Concrete data generates contracts can be in RDF, XML or JSON Use Identifiers and Tags for identifying and searchesAPSCC 2011, 12 Dec, 2011, Jeju, Korean 10
  11. 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 resultsAPSCC 2011, 12 Dec, 2011, Jeju, Korean 11
  12. 12. Prototype RDF for representing term categories, term names, term values, units Allegro Graph for storing contract knowledgeAPSCC 2011, 12 Dec, 2011, Jeju, Korean 12
  13. 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 APSCC 2011, 12 Dec, 2011, Jeju, Korean 13
  14. 14. Existingcommonknowledgeabout OpenDataCommons APSCC 2011, 12 Dec, 2011, Jeju, Korean 14
  15. 15. Step 2: provide OpenBuildingCO2 OpenBuildingCO2 by OpenGov for modifying quality of government data data and data right Data contract for green building dataAPSCC 2011, 12 Dec, 2011, Jeju, Korean 15
  16. 16. Experiments – composing data contract termsAPSCC 2011, 12 Dec, 2011, Jeju, Korean 16
  17. 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]APSCC 2011, 12 Dec, 2011, Jeju, Korean 17
  18. 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/truongAPSCC 2011, 12 Dec, 2011, Jeju, Korean 18