Data Warehousing and Knowledge Discovery
Author(s) -
Yahiko Kambayashi,
Mukesh Mohania,
Wolfram Wöß
Publication year - 2004
Publication title -
lecture notes in computer science
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.249
H-Index - 400
eISSN - 1611-3349
pISSN - 0302-9743
DOI - 10.1007/b99817
Subject(s) - data warehouse , knowledge extraction , computer science , data science , information retrieval , data mining
EXtensible Markup Language (XML) has emerged as the dominant standard in describing and exchanging data among heterogeneous data sources. XML with its self-describing hierarchical structure and its associated XML Schema (XSD) provides the flexibility and the manipulative power needed to accommodate complex, disconnected, heterogeneous data. The issue of large volume of data appearing deserves investigating XML Document Warehouses. But due to XML’s non-scalar, set-based semi-structured nature, traditional data design models lack the ability to represent XML design level constructs in an abstract and implementation-independent form which are crucial for designing complex domains such as data marts and data warehouses, but also during their operational and maintenance phase. We utilize Object Oriented (OO) concepts to develop a conceptual model for XML Document Warehouses. In this paper we propose a conceptual design formalism to build meaningful XML Document Warehouses (XDW). Our focus includes; (1) conceptually design and build meaningful XML (warehouse) repository (xFACT) using OO concepts in integration with XML Schema constructs, (2) conceptually model and design virtual dimensions using XML conceptual views [10a] [10b] to satisfy warehouse end-user requirements and (3) use UML package diagrams to help logically group and build hierarchical conceptual views to enhance semantics and expressiveness of the XDW.
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