Abstract

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.

Keywords: Data mining; Association rule mining; Query flock; Deductive databases; Recursive query evaluation

Article Outline

1. Introduction

1.1. Association rule mining

1.2. Query flocks

1.3. Query flock architecture

2. View evaluator

2.1. View evaluation with connection graphs

2.2. Algorithm: view evaluator

2.3. Example: unexplained side effects

3. External optimizer

3.1. Example: unexplained side effects

4. Translator

4.1. Algorithm: translator

4.2. Example: unexplained side effects

5. Prototype implementation and some experiments

6. Conclusion

References