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Additional resources for Data Warehousing Design and Advanced Engineering Applications: Methods for Complex Construction (Advances in Data Warehousing and Mining (Adwm))
183-197). , Nixon, B. , & Mylopoulos, J. (1999). Non-functional requirements in software. Berlin, Germany: Springer. , & Levesque, H. (1990). Intention is choice with commitment. Artificial Intelligence, 42(2-3), 213–261. 1016/00043702(90)90055-5 13 From User Requirements to Conceptual Design in Data Warehouse Design Cysneiros, L. , & Sampaio do Prado Leite, J. C. (2004). Nonfunctional requirements: From elicitation to conceptual models. IEEE Transactions on Software Engineering, 30(5), 328–350.
A DTD can be represented as an acyclic oriented graph with one node for each element definition. The links between two nodes are composition links. The attributes associated to the elements in a DTD are associated to element nodes in the graph representing to the DTD. Because the DTDs are acyclic, their associated graph may be represented as a forest of trees, whose roots correspond to entry points in the graph (nodes without predecessors). Nodes shared in the graph by several trees are duplicated in order to make these trees independent of each other.
The format of the mappings for the classes and the properties of O is described just below. , 2007) in the associated graph. e refers to an element in T, @att refers to the attribute att. 23 Data Extraction, Transformation and Integration Guided by an Ontology Figure 4. Example of a XML document conformed to the DTD tree of the Figure 3 Note that we may have conditional mappings when the link with an attribute att depends on its value val (C1 ↔ //e/[@att = ‘val’]/@att). Data Extraction and Transformation Data extraction and transformation are completely automatic tasks usually performed by wrappers.