Rules Extraction Based on Data Summarisation Approach Using DARA

Abstract

This paper helps the understanding and development of a data summarisation approach that summarises structured data stored in a non-target table that has many-to-one relations with the target table. In this paper, the feasibility of data summarisation techniques, borrowed from the Information Retrieval Theory, to summarise patterns obtained from data stored across multiple tables with one-to-many relations is demonstrated. The paper describes the Dynamic Aggregation of Relational Attributes (DARA) framework, which summarises data stored in non-target tables in order to facilitate data modelling efforts in a multi-relational setting. The application of the DARA algorithm involving structured data is presented in order to show the adaptability of this algorithm to real world problems.KeywordsSick LeaveRelational DatabaseVector Space ModelRule ExtractionMultiple TableThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.