Enhancing data quality in data warehouse environments
Communications of the ACM
Data quality: the field guide
A Framework for Analysis of Data Quality Research
IEEE Transactions on Knowledge and Data Engineering
Efficient dynamic mining of constrained frequent sets
ACM Transactions on Database Systems (TODS)
CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
An EffectiveMulti-Layer Model for Controlling the Quality of Data
IDEAS '07 Proceedings of the 11th International Database Engineering and Applications Symposium
FIsViz: a frequent itemset visualizer
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
An efficient system for detecting outliers from financial time series
BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
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Weather plays an important role in many areas such as agriculture. Having a clean set of agro-meteorological data removes doubt on weather-derived models and ensures confidence on decisions supported by these models. In this paper, we present our design and development of a prototype system for detecting abnormal weather observations. This system is applicable for the real-life data quality control and assurance of reliable and error-free agro-meteorological data. It does so by checking the internal validity, as well as the temporal and spatial consistency, of each weather observation. In addition to having the ability to detect abnormal observations and control the quality of weather data, our system also has the capability to estimate values for temporal and spatial weather parameters. Having such a capability is helpful in replacing incorrect data and filling missing values. Moreover, in this paper, we discuss the challenges and our solutions to problems related to the management of weather observations such as data gaps, format incompatibilities, and integration issues.