On compiling queries in recursive first-order databases
Journal of the ACM (JACM)
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 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
TopCat: Data Mining for Topic Identification in a Text Corpus
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Data mining techniques for structured and semistructured data
Data mining techniques for structured and semistructured data
TopCat: Data Mining for Topic Identification in a Text Corpus
IEEE Transactions on Knowledge and Data Engineering
Data management research at the Middle East Technical University
ACM SIGMOD Record
ILP-based concept discovery in multi-relational data mining
Expert Systems with Applications: An International Journal
Automated trend analysis of proteomics data using an intelligent data mining architecture
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Data mining can be defined as a process for finding trends and patterns in large data. An important technique for extracting useful information, such as regularities, from usually historical data, is called as association rule mining. Most research on data mining is concentrated on traditional relational data model. On the other hand, the query flocks technique, which extends the concept of association rule mining with a 'generate-and-test' model for different kind of patterns, can also be applied to deductive databases. In this paper, query flocks technique is extended with view definitions including recursive views. Although in our system query flock technique can be applied to a data base schema including both the intensional data base (IDB) or rules and the extensible data base (EDB) or tabled relations, we have designed an architecture to compile query flocks from datalog into SQL in order to be able to use commercially available data base management systems (DBMS) as an underlying engine of our system. However, since recursive datalog views (IDB's) cannot be converted directly into SQL statements, they are materialized before the final compilation operation. On this architecture, optimizations suitable for the extended query flocks are also introduced. Using the prototype system, which is developed on a commercial database environment, advantages of the new architecture together with the optimizations, are also presented.