Query optimization for semistructured data using path constraints in a deterministic data model

9Citations
Citations of this article
8Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Path constraints have been studied for semistructured data modeled as a rooted edge-labeled directed graph [4, 11–13]. In this model, the implication problems associated with many natural path constraints are undecidable [11, 13]. A variant of the graph model, called the deterministic data model, was recently proposed in [10]. In this model, data is represented as a graph with deterministic edge relations, i.e., the edges emanating from any node in the graph have distinct labels. This model is more appropriate for representing, e.g., ACeDB [27] databases and Web sites. This paper investigates path constraints for the deterministic data model. It demonstrates the application of path constraints to, among others, query optimization. Three classes of path constraints are considered: the language Pc introduced in [11], an extension of Pc, denoted by Pwc, by including wildcards in path expressions, and a generalization of Pwc, denoted by P*c, by representing paths as regular expressions. The implication problems for these constraint languages are studied in the context of the deterministic data model. It is shown that in contrast to the undecidability result of [11], the implication and finite implication problems for Pc are decidable in cubic-time and are finitely axiomatizable. Moreover, the implication problems are decidable for Pwc. However, the implication problems for P*c are undecidable.

Cite

CITATION STYLE

APA

Buneman, P., Fan, W., & Weinstein, S. (2000). Query optimization for semistructured data using path constraints in a deterministic data model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1949, pp. 208–223). Springer Verlag. https://doi.org/10.1007/3-540-44543-9_13

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free