In the Future Trends
section we describe the topics that are relevant in the semistructured data warehouse
context and sketch possible lines for future works, while in the Conclusion section
we summarize the chapter content.
Background
Semistructured.Data
Semistructured data have irregular structure, and rapidly evolving or missing schema
(Abiteboul, 1997). The classical example of semistructured data is related to data
stored on the World Wide Web: at a typical Web site, data are varied and irregular,
and the overall structure of the site changes often. Web data are integrated from
multiple, heterogeneous data sources, where discrepancies among various data
representations are likely.
In the semistructured data context, the database community has investigated flexible
data models to represent in a uniform way this kind of information. The results
of this research are a number of approaches in which labeled graphs are used to
represent semistructured data. These models organize data in graphs where nodes
denote objects or values, and edges represent relationships between them.
In this section, we briefly describe some semistructured data models based on labeled
graphs (Damiani & Tanca, 1997; Papakonstantinou et al., 1995), and the eXtensible
Markup Language (XML) (World Wide Web Consortium, 1998) which is spread-
280 Combi & Oliboni
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