On Evaluating and Publishing Data         Concerns for Data as a Service                  Hong-Linh Truong and Schahram Du...
Overview  Motivation and background  Data concern-aware service engineering   process  A framework for evaluating and p...
The rise of DaaS  Web services technologies and the cloud computing   model foster the concept of data/information as a s...
Examples of DaaSSource: http://www.undata-api.org/          Source:                                            http://www....
Motivation: the role of data             concerns               Should we perform data                                    ...
Motivation: service provider versus             data provider  The DaaS service provider is separated from the   data pro...
Example: DaaS provider =! data             provider   Source: http://www.infochimps.orgAPSCC 2010, Hangzhou 9 Dec 2010    ...
Background: data resources  Data items → data resources →   DaaS APIs → consumers  DaaS and data providers have the     ...
Backgroud: diverse concerns                           associated with service and dataHong-Linh Truong, Schahram Dustdar "...
Data concern-aware service             engineering process    Typical activities                                          ...
Wrapping, selecting, and updating             data in DaaS Typically different strategies for structured data and  unstru...
Evaluating data concerns (1)  Based on three concepts:     evaluation scope, evaluation modes and integration model  Ev...
Evaluating data concerns (2)Pull, pass-by-references                Pull, pass-by-valuesPush, pass-by-values APSCC 2010, H...
Publishing data concern             information  Off-line publishing of data concerns      suitable for static data conc...
How do we utilize the data concern-                  aware service engineering process?  Using this model we can determin...
QoD framework: pull QoD             evaluation models for DaaS  Pull QoD Evaluation Models for DaaS  Pass-by-references ...
QoD framework: publishing             concerns (1)  Off-line data concern   publishing       a common data concern      ...
QoD framework: publishing                 concerns (2) On-the-fly querying data concerns associated with data  resources ...
QoD framework: QoD monitoring             and composition  QoD concerns monitoring and composition are   useful for the e...
Experiments  Implementation      Java, JAX-RS/Jersey      Drools  Utilizing UNDataAPI - www.undata-api.org      XML d...
Experiment: evaluating and             annotating QoD metrics http://www.infosys.tuwien.ac.at/prototyp/SOD1/dataconcerns/A...
Experiments: publishing QoD with             data resourcesAPSCC 2010, Hangzhou 9 Dec 2010   22
Experiments: simple rules for             monitoring and composing QoDAPSCC 2010, Hangzhou 9 Dec 2010   23
Conclusions and future work  A novel, generic data concern-aware service engineering   process for DaaS  A proof-of-conc...
Thanks for your attention!             Hong-Linh Truong             Distributed Systems Group             Vienna Universit...
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On Evaluating and Publishing Data Concerns for Data as a Service

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On Evaluating and Publishing Data Concerns for Data as a Service

  1. 1. 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/truongAPSCC 2010, Hangzhou 9 Dec 2010 1
  2. 2. Overview  Motivation and background  Data concern-aware service engineering process  A framework for evaluating and publishing QoD of DaaS  Experiments  Conclusions and future workAPSCC 2010, Hangzhou 9 Dec 2010 2
  3. 3. 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.APSCC 2010, Hangzhou 9 Dec 2010 3
  4. 4. Examples of DaaSSource: 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
  5. 5. 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 concernsAPSCC 2010, Hangzhou 9 Dec 2010 5
  6. 6. 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 DaaSAPSCC 2010, Hangzhou 9 Dec 2010 6
  7. 7. Example: DaaS provider =! data provider Source: http://www.infochimps.orgAPSCC 2010, Hangzhou 9 Dec 2010 7
  8. 8. 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/RESTAPSCC 2010, Hangzhou 9 Dec 2010 8
  9. 9. Backgroud: diverse concerns associated with service and dataHong-Linh Truong, Schahram Dustdar "On Analyzing and Specifying Concerns for Data as a Service" , The 2009 Asia-Pacific Services ComputingConference (IEEE APSCC 2009), (c) IEEE Computer Society, December 7-11, 2009, Biopolis, Singapore. 9
  10. 10. Data concern-aware service engineering process Typical activities for data wrapping and publishing Typical activities for data updating & retrievalAPSCC 2010, Hangzhou 9 Dec 2010 10
  11. 11. 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 techniquesAPSCC 2010, Hangzhou 9 Dec 2010 11
  12. 12. 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 toolsAPSCC 2010, Hangzhou 9 Dec 2010 12
  13. 13. Evaluating data concerns (2)Pull, pass-by-references Pull, pass-by-valuesPush, pass-by-values APSCC 2010, Hangzhou 9 Dec 2010 13
  14. 14. 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 resourcesAPSCC 2010, Hangzhou 9 Dec 2010 14
  15. 15. 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, CyprusAPSCC 2010, Hangzhou 9 Dec 2010 15
  16. 16. 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 toolsAPSCC 2010, Hangzhou 9 Dec 2010 16
  17. 17. 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 systemsAPSCC 2010, Hangzhou 9 Dec 2010 17
  18. 18. 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: 347-359APSCC 2010, Hangzhou 9 Dec 2010 18
  19. 19. 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 metricsAPSCC 2010, Hangzhou 9 Dec 2010 19
  20. 20. 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 UNDataAPIAPSCC 2010, Hangzhou 9 Dec 2010 20
  21. 21. Experiment: evaluating and annotating QoD metrics http://www.infosys.tuwien.ac.at/prototyp/SOD1/dataconcerns/APSCC 2010, Hangzhou 9 Dec 2010 21
  22. 22. Experiments: publishing QoD with data resourcesAPSCC 2010, Hangzhou 9 Dec 2010 22
  23. 23. Experiments: simple rules for monitoring and composing QoDAPSCC 2010, Hangzhou 9 Dec 2010 23
  24. 24. 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 ?APSCC 2010, Hangzhou 9 Dec 2010 24
  25. 25. 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.atAPSCC 2010, Hangzhou 9 Dec 2010 25

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