FROM QoD TO QoS - Data Quality Issues in Cloud Computing

Przemyslaw Pawluk, Marin Litoiu, Nick Cercone

2011

Abstract

The concept of Quality of Data (QoD) has so far been neglected in the context of cloud computing. It was; however explored for the long time in the context of data exchange, data integration and information systems. Well established approaches like Total Data Quality Management, Data Warehouse Quality or Data Quality in Cooperative Information Systems have been proposed to calculate, store and maintain information about QoD. On the other hand concept of Quality of Service has been investigated in the context of Internet Systems, multimedia transmission and enterprise systems. It was also investigated in connection to cloud computing. The main goal of this work is to show direct connection between QoD and QoS. We show that assuring high QoD is necessary to achieve high QoS. We also identify major shortcomings of public cloud vendors in terms of provided configuration management data.

References

  1. Amazon (2011a). Amazon ec2. http://aws.amazon.com/ec 2/.
  2. Amazon (2011b). Amazon web service. http://aws.amazon. com.
  3. Armstrong, D. and Djemame, K. (2009). Towards quality of service in the cloud. In Proceedings of 25th UK Performance Engineering Workshop.
  4. Azure (2011). Azure. http://www.microsoft.com/azure.
  5. Ballou, D. and Pazer, H. (1985). Modeling data and process quality in multi-input, multi-output information systems. Management Science, 31(2):150-162.
  6. Batini, C. and Scannapieco, M. (2006). Data Quality: Concepts, Methodologies and Techniques. DataCentric Systems and Applications. Springer-Verlag New York, Inc., Secaucus, NJ, USA.
  7. Berti-Ó quille, L. (2007). Data quality awareness: a case study for cost optimal association rule mining. Knowl. Inf. Syst., 11:191-215.
  8. Berti-Equille, L. and Moussouni, F. (2005). Quality-Aware Integration and Warehousing of Genomic Data. In Proceedings of the 2005 International Conference on Information Quality.
  9. Bobrowski, M., Marr, M., and Yankelevich, D. (1998). A software engineering view of data quality. In European Quality Week Conference.
  10. Campbell, A. T. (1996). A Quality of Service Architecture. PhD thesis, Lancaster University.
  11. Dasu, T. and Johnson, T. (2003). Exploratory Data Mining and Data Cleaning. Wiley-Interscience.
  12. EMA (2008). How to define detailed requirements for your enterprise cmdb project: A hands-on workbook.
  13. Gertz, M. and Schmitt, I. (1998). Data Integration Techniques based on Data Quality Aspects. In Schmitt, I., Tü rker, C., Hildebrandt, E., and Hö ding, M., editors, Proceedings 3. Workshop “Föderierte Datenbanken”, Magdeburg, 10./11. Dezember 1998, pages 1-19. Shaker Verlag, Aachen.
  14. Google (2011a). Google app engine. http://code.google. com/appengine.
  15. Google (2011b). Google apps. http://www.google.com/ apps/business.
  16. Harzog, B. (2010). Is the cmdb irrelevant in a virtual and cloud based world? Blog entry: http://www.virtualizationpractice.com/blog/?p=5726.
  17. IBM (2011a). Ibm cloud computing. http://www935.ibm.com/services/us/cloud/index.html.
  18. IBM (2011b). Ibm computing on demand. http://www03.ibm.com/systems/deepcomputing/cod/.
  19. ISO (1994). ISO 8402 Quality Management and Quality Assurance: Vocabulary. ISO. Withdrawn standard.
  20. ISO (2005). ISO 9000:2005 Quality management systems - Fundamentals and vocabulary. ISO. Published standard.
  21. ISO/IEC (1998). ISO/IEC 13236:1998. Information technology - Quality of service: Framework. ISO/IEC.
  22. Kriebel, C. H. and Moore, J. H. (1982). Economics and management information systems. SIGMIS Database, 14(1):30-40.
  23. Lim, H. C., Babu, S., Chase, J. S., and Parekh, S. S. (2009). Automated control in cloud computing: challenges and opportunities. In Proceedings of the 1st workshop on Automated control for datacenters and clouds, ACDC 7809, pages 13-18, New York, NY, USA. ACM.
  24. Marti, P., Fuertes, J. M., and Fohler, G. (2002). Improving quality-of-control using flexible timing constraints: Metric and scheduling issues. In In IEEE RTSS.
  25. Naumann, F. (2002). Quality-driven query answering for integrated information systems. Springer-Verlag New York, Inc., New York, NY, USA.
  26. Orr, K. (1998). Data quality and system theory. Commun. ACM, 41(2):66-71.
  27. Parssian, A., Sarkar, S., and Jacob, V. S. (2002). Assessing information quality for the composite relational operation join. In IQ, pages 225-237.
  28. Reddy, M. P. and Wang, R. Y. (1995). Estimating data accuracy in a federated database environment. In CISMOD, pages 115-134.
  29. Row, J. R. (2010). All about cloud computing and data quality. http://www.brighthub.com.
  30. Segev, A. and Fang, W. (1990). Currency-based updates to distributed materialized views. In Proceedings of the Sixth International Conference on Data Engineering, pages 512-520, Washington, DC, USA. IEEE Computer Society.
  31. Tayi, G. K. and Ballou, D. P. (1998). Examining data quality. Commun. ACM, 41(2):54-57.
  32. Tupek, A. R. (2006). Definition of data quality.
  33. Vambenepe, W. (2010). Cmdb in the cloud: not your fathers cmdb. Blog entry: http://stage.vambenepe.com/archives/1527.
  34. Vaquero, L. M., Rodero-Merino, L., Caceres, J., and Lindner, M. (2008). A break in the clouds: towards a cloud definition. SIGCOMM Comput. Commun. Rev., 39:50-55.
  35. Vouk, M. A. (2008). Cloud computing issues, research and implementations. ITI 2008 30th International Conference on Information Technology Interfaces, 16(4):31- 40.
  36. Wand, Y. and Wang, R. Y. (1996). Anchoring data quality dimensions in ontological foundations. Commun. ACM, 39(11):86-95.
  37. Wang, R. Y., Pierce, E. M., and Madnick, S. E. (2005). Information quality, volume 1 of Advances in management information systems: Information Quality. M.E. Sharpe.
  38. Wang, R. Y. and Strong, D. M. (1996). Beyond accuracy: what data quality means to data consumers. J. Manage. Inf. Syst., 12(4):5-33.
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Paper Citation


in Harvard Style

Pawluk P., Litoiu M. and Cercone N. (2011). FROM QoD TO QoS - Data Quality Issues in Cloud Computing . In Proceedings of the 1st International Conference on Cloud Computing and Services Science - Volume 1: IDQ, (CLOSER 2011) ISBN 978-989-8425-52-2, pages 697-702. DOI: 10.5220/0003558606970702


in Bibtex Style

@conference{idq11,
author={Przemyslaw Pawluk and Marin Litoiu and Nick Cercone},
title={FROM QoD TO QoS - Data Quality Issues in Cloud Computing},
booktitle={Proceedings of the 1st International Conference on Cloud Computing and Services Science - Volume 1: IDQ, (CLOSER 2011)},
year={2011},
pages={697-702},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003558606970702},
isbn={978-989-8425-52-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Cloud Computing and Services Science - Volume 1: IDQ, (CLOSER 2011)
TI - FROM QoD TO QoS - Data Quality Issues in Cloud Computing
SN - 978-989-8425-52-2
AU - Pawluk P.
AU - Litoiu M.
AU - Cercone N.
PY - 2011
SP - 697
EP - 702
DO - 10.5220/0003558606970702