Towards an efficient evaluation of general queries: quantifier and disjunction processing revisited
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
Fast algorithms for universal quantification in large databases
ACM Transactions on Database Systems (TODS)
Providing better support for a class of decision support queries
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Range nesting: a fast method to evaluate quantified queries
SIGMOD '83 Proceedings of the 1983 ACM SIGMOD international conference on Management of data
Processing queries with quantifiers a horticultural approach
PODS '83 Proceedings of the 2nd ACM SIGACT-SIGMOD symposium on Principles of database systems
Improving SQL with Generalized Quantifiers
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
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
StreamJoin: A Generic Database Approach to Support the Class of Stream-Oriented Applications
IDEAS '00 Proceedings of the 2000 International Symposium on Database Engineering & Applications
Evaluating Universal Quantification in XML
IEEE Transactions on Knowledge and Data Engineering
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Queries containing universal quantification are used in many applications, including business intelligence applications. Several algorithms have been proposed to implement universal quantification efficiently. These algorithms are presented in an isolated manner in the research literature - typically, no relationships are shown between them. Furthermore, each of these algorithms claims to be superior to others, but in fact each algorithm has optimal performance only for certain types of input data. In this paper, we present a comprehensive survey of the structure and performance of algorithms for universal quantification. We introduce a framework for classifying all possible kinds of input data for universal quantification. Then we go on to identify the most efficient algorithm for each such class. One of the input data classes has not been covered so far. For this class, we propose several new algorithms. For the first time, we are able to identify the optimal algorithm to use for any given input dataset. These two classifications of input data and optimal algorithms are important for query optimization. They allow a query optimizer to make the best selection when optimizing at intermediate steps for the quantification problem.