Abstract
The quality of data is often defined as “fitness for use”, the ability of data collection to meet users’ requirements. The assessment of data quality dimensions should consider the degree to which data satisfy users’ needs. User expectations are clearly related to the selected information and at the same time the information can have different utilities depending on the type of users that accesses it. In this thesis, the information is considered as a product of a specific service and data quality as a component of the service quality. For each service, it is possible to identify a provider and a final user. In the data quality literature, authors have always only considered as important the final users’ perspective declaring that providers should adapt their service offerings in order to completely satisfy users’ requirements. This paper presents a utility-based model of the provider and customers’ interests developed on the basis of multi-class offerings. The model is exploited to analyze the optimal service offerings that allow the efficient allocation of quality improvements in activities for the provider.
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References
Batini C, Cabitza F, Cappiello C, Francalanci C (2008) A comprehensive data quality methodology for web and structured data [J]. Int J Innov Comput Appl 1(3):205–218
Cappidlo C, Ficiarop PB (2006) HIQM: a methodology for information quality monitoring, measurement and improvement [C]. In: 25th international conference on conceptual modeling, Arizona, Springer, Berlin/Heidelberg, 2006, pp 339–351
English LP (1999) Improving data warehouse and business information quality [M]. Wiley, New York
Evans JR, Lindsay WM (2005) The management and control of quality [M]. South-Western, Division of Thomson Learning, Cincinnati
Jarke M, Jeusfeld MA, Quix C, Vassiliadis P (1999) Architecture and quality in data warehouses: an extended repository approach [J]. Inf Syst 24(3):229–253
Klemperer P (1999) Auction theory: a guide to the literature [J]. J Econ Surv 13(3):227–286
Martin JE, Markus H (2004) A framework for the classification of data quality costs and an analysis of their progression [C]. In: Proceedings of the International Conference on Information Quality (ICIQ), 2004, Cambridge, pp 311–325
Naumann F (2002) Quality-driven query answering for integrated information systems [M]. Springer, New York
Orr K (1998) Data quality and systems theory [J]. Commun ACM 41(2):66–71
Redman T (1996) Data quality for the information age [M]. Artech House, Boston
Scannapieco M, Catarci T (2002) Data quality under a computer science perspective [J]. Arch Comput 12(2):1–15
Wand Y, Wang R (1996) Anchoring data quality dimensions in ontological foundations [J]. Commun ACM 39(11):86–95
Wang RY (1998) A product perspective on total data quality management [J]. Commun ACM 41(2):58–65
Wang RY, Strong DM (1996) Beyond accuracy: what data quality means to data consumers [J]. J Manage Inf Syst 12(4):5–34
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Jiang, H., Liu, Jx., Zhang, Y., Yu, Ch. (2013). An Exploration of Data Quality Management Based on Allocation Efficiency Model. In: Qi, E., Shen, J., Dou, R. (eds) Proceedings of 20th International Conference on Industrial Engineering and Engineering Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40072-8_94
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DOI: https://doi.org/10.1007/978-3-642-40072-8_94
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