A unified framework for heterogeneous patterns

Knowledge patterns, such as association rules, clusters or decision trees, can be defined as concise and relevant information that can be extracted, stored, analyzed, and manipulated by knowledge workers in order to drive and specialize business decision processes. In this paper we deal with data mining patterns. The ability to manipulate different types of patterns under a unified environment is becoming a fundamental issue for any ‘intelligent’ and data-intensive application. However, approaches proposed so far for pattern management usually deal with specific and predefined types of patterns and mainly concern pattern extraction and exchange issues. Issues concerning the integrated, advanced management of heterogeneous patterns are in general not (or marginally) taken into account. What is missing is therefore a unified framework dealing with heterogeneous patterns in an homogeneous way. This work addresses this problem by proposing a general framework for heterogeneous pattern representation and management under a unified perspective. After discussing the motivations underlying our work, a formal pattern model as well as query and manipulation languages are presented. Complexity issues of the proposed framework are also investigated and a proposal for an SQL-based implementation of the framework is finally provided.