Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Fast discovery of association rules
Advances in knowledge discovery and data mining
Composition of Mining Contexts for Efficient Extraction of Association Rules
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Relational Association Rules: Getting WARMeR
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Why is the snowflake schema a good data warehouse design?
Information Systems
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Extracting semantics from data cubes using cube transversals and closures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining all frequent projection-selection queries from a relational table
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Mining frequent conjunctive queries in star schemas
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Computing Supports of Conjunctive Queries on Relational Tables with Functional Dependencies
Fundamenta Informaticae
An efficient computation of frequent queries in a star schema
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Mining frequent conjunctive queries using functional and inclusion dependencies
The VLDB Journal — The International Journal on Very Large Data Bases
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The problem of mining all frequent queries in a database is intractable, even if we consider conjunctive queries only. In this paper, we study this problem under reasonable restrictions on the database, namely: (i) the database scheme is a star scheme; (ii) the data in the database satisfies a set of functional dependencies and a set of referential constraints. We note that star schemes are considered to be the most appropriate schemes for data warehouses, while functional dependencies and referential constraints are the most common constraints that one encounters in real databases. Our approach is based on the weak instance semantics of databases and considers the class of selection-projection queries over weak instances. In such a context, we show that frequent queries can be mined using level-wise algorithms such as Apriori.