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)
Set-oriented data mining in relational databases
Data & Knowledge Engineering
Providing better support for a class of decision support queries
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Optimizing the performance of a relational algebra database interface
Communications of the ACM
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
Remarks on the algebra of non first normal form relations
PODS '82 Proceedings of the 1st ACM SIGACT-SIGMOD symposium on Principles of database systems
Divide-and-Conquer Algorithm for Computing Set Containment Joins
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
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
Set Containment Joins: The Good, The Bad and The Ugly
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
Evaluation of Main Memory Join Algorithms for Joins with Set Comparison Join Predicates
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
Efficient storage and query processing of set-valued attributes
Efficient storage and query processing of set-valued attributes
Processing frequent itemset discovery queries by division and set containment join operators
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Hi-index | 0.00 |
Queries containing universal quantification are used in many applications, including business intelligence applications and in particular data mining. We present a comprehensive survey of the structure and performance of algorithms for universal quantification. We introduce a framework that results in a complete classification 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. Thus, for the first time, we are able to identify the optimal algorithm to use for any given input dataset.These two classifications of optimal algorithms and input data are important for query optimization. They allow a query optimizer to make the best selection when optimizing at intermediate steps for the quantification problem.In addition to the classification, we show the relationship between relational division and the set containment join and we illustrate the usefulness of employing universal quantifications by presenting a novel approach for frequent itemset discovery.