Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
Fast algorithms for universal quantification in large databases
ACM Transactions on Database Systems (TODS)
Optimizing the performance of a relational algebra database interface
Communications of the ACM
Universal Quantification in Relational Databases: A Classification of Data and Algorithms
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Supporting Universal Quantification in a Two-Dimensional Database Query Language
Proceedings of the Sixth International Conference on Data Engineering
HAS, a Relational Algebra Operator or Divide is not Enough to Conquer
Proceedings of the Second International Conference on Data Engineering
Improving SQL with Generalized Quantifiers
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Optimizing Queries with Universal Quantification in Object-Oriented and Object-Relational Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Logical Rewritings for Improving the Evaluation of Quantified Queries
MFDBS '89 Proceedings of the 2nd Symposium on Mathematical Fundamentals of Database Systems
The VLDB Journal — The International Journal on Very Large Data Bases
Structural Joins: A Primitive for Efficient XML Query Pattern Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Efficient structural joins on indexed XML documents
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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Queries posed to database systems often involve Universal Quantification. Such queries are typically expensive to evaluate. While they can be handled by basic access methods, for selection, grouping, etc., new access methods specifically tailored to evaluate universal quantification can greatly decrease the computational cost. In this paper, we study the efficient evaluation of universal quantification in an XML database. Specifically, we develop a small taxonomy of universal quantification types, and define a family of algorithms suitable for handling each. We experimentally demonstrate the performance benefits of the new family of algorithms.