Decision making activities stress data and information quality requirements. The quality of data sources is frequently very poor, therefore a cleansing process is required before using such data for decision making processes. When alternative (and more trusted) data sources are not available data can be cleansed only us- ing business rules derived from domain knowledge. Business rules focus on fixing inconsistencies, but an inconsistency can be cleansed in different ways (i.e. the correction can be not deterministic), therefore the choice on how to cleanse data can (even strongly) affect the aggregate values computed for decision making purposes. The paper proposes a methodology exploiting Finite State Systems to quantitatively estimate how computed variables and indicators might be affected by the uncertainty related to low data quality, indepen- dently from the data cleansing methodology used. The methodology has been implemented and tested on a real case scenario providing effective results.

Mezzanzanica, M., Boselli, R., Cesarini, M., Mercorio, F. (2012). Data Quality Sensitivity Analysis on Aggregate Indicators. In DATA 2012 - Proceedings of the International Conference on Data Technologies and Applications (pp.97-108). SciTePress [10.5220/0004040300970108].

Data Quality Sensitivity Analysis on Aggregate Indicators

MEZZANZANICA, MARIO;BOSELLI, ROBERTO;CESARINI, MIRKO;MERCORIO, FABIO
2012

Abstract

Decision making activities stress data and information quality requirements. The quality of data sources is frequently very poor, therefore a cleansing process is required before using such data for decision making processes. When alternative (and more trusted) data sources are not available data can be cleansed only us- ing business rules derived from domain knowledge. Business rules focus on fixing inconsistencies, but an inconsistency can be cleansed in different ways (i.e. the correction can be not deterministic), therefore the choice on how to cleanse data can (even strongly) affect the aggregate values computed for decision making purposes. The paper proposes a methodology exploiting Finite State Systems to quantitatively estimate how computed variables and indicators might be affected by the uncertainty related to low data quality, indepen- dently from the data cleansing methodology used. The methodology has been implemented and tested on a real case scenario providing effective results.
No
paper
Scientifica
Data Quality, Data Cleansing, Sensitivity Analysis, Inconsistent Databases, Aggregate Indicators, Uncertainty Assessment
English
International Conference on Data Technologies and Applications (DATA) 25/27 july
978-989-8565-18-1
www.dataconference.org
Mezzanzanica, M., Boselli, R., Cesarini, M., Mercorio, F. (2012). Data Quality Sensitivity Analysis on Aggregate Indicators. In DATA 2012 - Proceedings of the International Conference on Data Technologies and Applications (pp.97-108). SciTePress [10.5220/0004040300970108].
Mezzanzanica, M; Boselli, R; Cesarini, M; Mercorio, F
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/36311
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