A semantic approach to ETL technologies

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• 作者：Sonia Bergamaschi^a ; ^{sonia.bergamaschi@unimore.it"" rel=""nofollow} ; Francesco Guerra^a ; ^{francesco.guerra@unimore.it"" rel=""nofollow} ; Mirko Orsini^a ; ^{mirko.orsini@unimore.it"" rel=""nofollow} ; Claudio Sartori^b ; ^{claudio.sartori@unibo.it"" rel=""nofollow} ; Maurizio Vincini^a ; ^{maurizio.vincini@unimore.it"" rel=""nofollow} [Author vitae]
• 关键词：Semantic ETL ; Semantic mapping ; Cluster analysis ; Data analysis ; Semantic data integration ; Data warehouse
• 刊名：Data and Knowledge Engineering
• 出版年：2011
• 出版时间：August 2011
• 年：2011
• 卷：70
• 期：8
• 页码：717-731
• 全文大小：1542 K

文摘

Data warehouse architectures rely on extraction, transformation and loading (ETL) processes for the creation of an updated, consistent and materialized view of a set of data sources. In this paper, we support these processes by proposing a tool that: (1) allows the semi-automatic definition of inter-attribute semantic mappings, by identifying the parts of the data source schemas which are related to the data warehouse schema, thus supporting the extraction process; and (2) groups the attribute values semantically related thus defining a transformation function for populating with homogeneous values the data warehouse.

Our proposal couples and extends the functionalities of two previously developed systems: the MOMIS integration system and the RELEVANT data analysis system. The system has been experimented within a real scenario concerning the creation of a data warehouse for enterprises working in the beverage and food logistic area. The results showed that the coupled system supports effectively the extraction and transformation processes.