Abstract
Quality of information benefits both on line transactional processing and on line analytical processing. However, quality assurance processes are mostly human intensive and the literature provides limited support to their automation. This paper proposes a rule-based data monitoring and improvement approach as a first step towards self-management of quality of data. These rules specify when to trigger both assessment procedures and improvement actions (e.g. data cleaning), on the basis of the actions performed on the databases and specific quality requirements associated with queries performed by users. They also capture all the events occurring as a consequence of data quality problems and alert the Quality Administrator if human involvement is required. Rules are classified and formalized in the paper. The overall data quality monitoring and improvement process is explained with examples.
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Ballou, D.P., Wang, R.Y., Pazer, H.L., Tayi, G.K.: Modelling information manufacturing systems to determine information product quality. Management Science 44(4) (1998)
Bovee, M., Srivastava, R.P., Mak, B.: A conceptual framework and belief- function approach to assessing overall information quality. In: Proceedings of the Sixth International Conference on Information Quality, November 2001. MIT Press, Cambridge (2001)
Cappiello, C., Francalanci, C., Pernici, B.: Time-related factors of data quality in multichannel information systems. Journal of Management Information Systems 20(3), 71–91 (2004)
Cappiello, C., Francalanci, C., Pernici, B., Plebani, P., Scannapieco, M.: Data quality assurance in cooperative information systems: a multi-dimension quality certificate. In: Proceedings of the International Workshop on Data Quality in Cooperative Information Systems (DQCIS 2003) (January 2003)
Deutsch, A., Fernandez, M., Florescu, D., Levy, A.: Xml-ql: A query language for xml. In: Proceedings of the 8th International World Wide Web Conference (1999)
English, L.P.: Improving Data Warehouse and Business Information Quality. John Wiley & Sons, Chichester (1999)
Eppler, M.J.: Managing Information Quality. Springer, Heidelberg (2003)
Hernandez, M., Stolfo, S.: The merge/purge problem for large databases. In: Proceedings ACM SIGMOD International Conference Management of Data (1995)
Jarke, M., Lenzerini, M., Vassiliou, Y., Vassiliadis, P.: Fundamentals of Data Warehouse. Springer, Heidelberg (2000)
Milano, D., Scannapieco, M., Catarci, T.: Quality-driven query processing of xquery queries. In: Proceedings of the International Workshop on Data and Information Quality (DIQ 2004) in conjunction with the CAiSE, pp. 78–89 (2004)
Naumann, F.: Quality-Driven Query Answering for Integrated Information Systems. LNCS, vol. 2261. Springer, Heidelberg (2002)
Naumann, F., Freytag, J.C., Leser, U.: Completeness of integrated information sources. Information Systems 29(7), 583–615 (2004)
Redman, T.C.: Data Quality for the Information Age. Artech House (1996)
Scannapieco, M., Pierce, E., Pernici, B.: Ip-uml: Towards a methodology for quality improvement based on the ip-map framework. AMIS (Advances in Management Information Systems) Monograph on Information Quality (2005)
Scannapieco, M., Virgillito, A., Marchetti, M., Mecella, M., Baldoni, R.: The daquincis architecture: a platform for exchanging and improving data quality in cooperative information systems. Information Systems 29(7), 551–582 (2004)
Shankaranarayan, G., Wang, R.Y., Ziad, M.: Modeling the manufacture of an information product with ip-map. In: Proceedings of the 6th International Conference on Information Quality (2000)
Wand, Y., Wang, R.Y.: Anchoring data quality dimensions in ontological foundations. Communication of the ACM 39(11) (1996)
Wang, R.Y.: A product perspective on total data quality management. Communications of the ACM 41(2) (1998)
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Cappiello, C., Francalanci, C., Pernici, B. (2005). A Self-monitoring System to Satisfy Data Quality Requirements. In: Meersman, R., Tari, Z. (eds) On the Move to Meaningful Internet Systems 2005: CoopIS, DOA, and ODBASE. OTM 2005. Lecture Notes in Computer Science, vol 3761. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11575801_37
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DOI: https://doi.org/10.1007/11575801_37
Publisher Name: Springer, Berlin, Heidelberg
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