Fuzzy logic and neural network handbook
Fuzzy logic and neural network handbook
Anchoring data quality dimensions in ontological foundations
Communications of the ACM
A product perspective on total data quality management
Communications of the ACM
Improving data warehouse and business information quality: methods for reducing costs and increasing profits
Data Quality for the Information Age
Data Quality for the Information Age
Fuzzy Multiple Attribute Decision Making: Methods and Applications
Fuzzy Multiple Attribute Decision Making: Methods and Applications
Advanced fault analysis in web service composition
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Quality analysis of composed services through fault injection
Information Systems Frontiers
Quality analysis of composed services through fault injection
BPM'07 Proceedings of the 2007 international conference on Business process management
A multidimensional analysis of data quality for credit risk management: New insights and challenges
Information and Management
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Hybrid Information Quality Management (HIQM) methodology is conceived to be a support to solve run-time data quality problems. The analysis of the business processes and context in the design phase allows identifying critical points in the business tasks in which information quality might be improved. In these points, information quality blocks have to be inserted in order to continuously monitor the information flows. Through suitable checks, failures due to information quality problems can be detected. Furthermore, failures and warnings in service execution may depend on a wide variety of causes. Along the causes, the methodology also produces a list of the suitable recovery actions for a timely intervention and quality improvement. The methodology is explained by means of a running example.