Abstract
The paper introduces the Robust Data Quality Analysis which exploits formal methods to support Data Quality Improvement Processes. The proposed methodology can be applied to data sources containing sequences of events that can be modelled by Finite State Systems. Consistency rules (derived from domain business rules) can be expressed by formal methods and can be automatically verified on data, both before and after the execution of cleansing activities. The assessment results can provide useful information to improve the data quality processes. The paper outlines the preliminary results of the methodology applied to a real case scenario: the cleansing of a very low quality database, containing the work careers of the inhabitants of an Italian province. The methodology has proved successful, by giving insights on the data quality levels and by providing suggestions on how to ameliorate the overall data quality process.
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References
Afrati, F.N., Kolaitis, P.G.: Repair checking in inconsistent Databases: Algorithms and Complexity. In: Proceedings of the 12th International Conference on Database Theory, ICDT 2009, pp. 31–41. ACM, New York (2009)
Barateiro, J., Galhardas, H.: A Survey of Data Quality Tools. Datenbank-Spektrum 14, 15–21 (2005)
Batini, C., Cappiello, C., Francalanci, C., Maurino, A.: Methodologies for Data Quality Assessment and Improvement. ACM Comput. Surv. 41, 16:1–16:52 (2009)
Batini, C., Scannapieco, M.: Data Quality: Concepts, Methodologies and Techniques. In: Data-Centric Systems and Applications. Springer, Heidelberg (2006)
Burch, J.R., Clarke, E.M., McMillan, K.L., Dill, D.L., Hwang, L.J.: Symbolic Model Checking: 1020 States and beyond. Inf. Comput. 98(2), 142–170 (1992)
Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. The MIT Press, Cambridge (1999)
CMurphi web page, http://www.dsi.uniroma1.it/~tronci/cached.murphi.html
CRISP Research Center web page, http://www.crisp-org.it
Della Penna, G., Intrigila, B., Melatti, I., Minichino, M., Ciancamerla, E., Parisse, A., Tronci, E., Venturini Zilli, M.: Automatic Verification of a Turbogas Control System with the Murϕ Verifier. In: Maler, O., Pnueli, A. (eds.) HSCC 2003. LNCS, vol. 2623, pp. 141–155. Springer, Heidelberg (2003)
Dovier, A., Quintarelli, E.: Applying Model-checking to solve Queries on semistructured Data. Computer Languages, Systems & Structures 35(2), 143–172 (2009)
Embury, S.M., Missier, P., Sampaio, S., Greenwood, R.M., Preece, A.D.: Incorporating Domain-Specific Information Quality Constraints into Database Queries. J. Data and Information Quality 1, 11:1–11:31 (2009)
Fan, W., Geerts, F., Jia, X.: A Revival of Integrity Constraints for Data Cleaning. Proc. VLDB Endow. 1, 1522–1523 (2008)
Gill, A.: Introduction to the Theory of Finite-state Machines. McGraw-Hill, New York (1962)
Khoussainov, B., Nerode, A.: Automata Theory and Its Applications. Birkhauser, Boston (2001)
Maletic, J., Marcus, A.: Data cleansing: beyond Integrity Analysis. In: Proceedings of the Conference on Information Quality, pp. 200–209 (2000)
Martini, M., Mezzanzanica, M.: The Federal Observatory of the Labour Market in Lombardy: Models and Methods for the Costruction of a Statistical Information System for Data Analysis. In: Larsen, C., Mevius, M., Kipper, J., Schmid, A. (eds.) Information Systems for Regional Labour Market Monitoring - State of the Art and Prospectives. Rainer Hampp Verlag (2009)
Müller, H., Freytag, J.C.: Problems, Methods and Challenges in Comprehensive Data Cleansing. Technical Report HUB-IB-164, Humboldt-Universität zu Berlin, Institut für Informatik (2003)
Murphi web page, http://sprout.stanford.edu/dill/murphi.html
Scannapieco, M., Missier, P., Batini, C.: Data Quality at a Glance. Datenbank-Spektrum 14, 6–14 (2005)
Vardi, M.Y.: Automata Theory for Database Theoreticians. Theoretical Studies in Computer Science, pp. 153–180. Academic Press Professional, Inc., London (1992)
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Mezzanzanica, M., Boselli, R., Cesarini, M., Mercorio, F. (2011). Data Quality through Model Checking Techniques. In: Gama, J., Bradley, E., Hollmén, J. (eds) Advances in Intelligent Data Analysis X. IDA 2011. Lecture Notes in Computer Science, vol 7014. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24800-9_26
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DOI: https://doi.org/10.1007/978-3-642-24800-9_26
Publisher Name: Springer, Berlin, Heidelberg
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