Abstract
Fuzzy Data Warehouse (FDW) is a data repository, which contains fuzzy data and allows fuzzy processing of the data. Incorporation of fuzziness into data warehouse systems gives the opportunity to process data at higher level of abstraction and improves the analysis of imprecise data. It also gives the possibility to express business indicators in natural language using terms, like: high, low, about 10, almost all, etc., represented by appropriate membership functions. Fuzzy processing in data warehouses can affect many operations, like data selection, filtering, aggregation, and grouping. In the paper, we concentrate on various cases of data aggregation in our recently implemented fuzzy data warehouse storing consumption and requirement for global natural resources represented as crisp and fuzzy measures. We show several examples of data aggregation and filtering using the extended syntax of the SQL SELECT statement.
The research presented here were done as a part of research and development project no. O R00 0068 07 and have been supported by Ministry of Science and Higher Education funds in the years 2009-2011.
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Małysiak-Mrozek, B., Mrozek, D., Kozielski, S. (2010). Processing of Crisp and Fuzzy Measures in the Fuzzy Data Warehouse for Global Natural Resources. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13033-5_63
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DOI: https://doi.org/10.1007/978-3-642-13033-5_63
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