Data exploration systems for databases

Abstract

Data exploration systems apply machine learning techniques, multivariate statistical methods, information theory, and database theory to databases in order to identify significant relationships among the data, and summarize information. The result of applying data exploration systems should create a better understanding of the structure of the data and a perspective of the data, enabling an analyst to form hypotheses for interpreting the data. This article argues that data exploration systems need a minimum amount of domain knowledge to guide both the statistical strategy and the interpretation of the resulting patterns discovered by these systems.