Abstract
Data Warehouses provide sophisticated tools for analyzing complex data online, in particular by aggregating data along dimensions spanned by master data. Changes to these master data is a frequent threat to the correctness of OLAP results, in particular for multi- period data analysis, trend calculations, etc. As dimension data might change in underlying data sources without notifying the data warehouse we are exploring the application of data mining techniques for detecting such changes and contribute to avoiding incorrect results of OLAP queries. © Springer-Verlag 2004.
Cite
CITATION STYLE
Eder, J., Koncilia, C., & Mitsche, D. (2004). Analysing slices of data warehouses to detect structural modifications. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3084, 492–505. https://doi.org/10.1007/978-3-540-25975-6_35
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