The authors propose an approach that provides a theoretical foundation for the use of object-oriented databases and object-relational databases in data warehouse, multidimensional database, and online analytical processing applications. This approach introduces a set of minimal constraints and extensions to the Unified Modeling Language for representing multidimensional modeling properties for these applications. Multidimensional modeling offers two benefits. First, the model closely parallels how data analyzers think and, therefore, helps users understand data. Second, multidimensional modeling helps predict what final users want to do, thereby facilitating performance improvements. The authors are using their approach to create an automatic implementation of a multidimensional model. They plan to integrate commercial online-analytical-processing tool facilities within their GOLD model case tool as well, a task that involves data warehouse prototyping and sample data generation issues.

This paper presents an algorithm for identifying noun phrase antecedents of third person personal pronouns, demonstrative pronouns, reflexive pronouns, and omitted pronouns (zero pronouns) in unrestricted Spanish texts. We define a list of constraints and preferences for different types of pronominal expressions, and we document in detail the importance of each kind of knowledge (lexical, morphological, syntactic, and statistical) in anaphora resolution for Spanish. The paper also provides a definition for syntactic conditions on Spanish NP-pronoun noncoreference using partial parsing. The algorithm has been evaluated on a corpus of 1,677 pronouns and achieved a success rate of 76.8%. We have also implemented four competitive algorithms and tested their performance in a blind evaluation on the same test corpus. This new approach could easily be extended to other languages such as English, Portuguese, Italian, or Japanese.